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Occupational Injury Risk Mitigation: Machine Learning Approach and Feature Optimization for Smart Workplace Surveillance

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Forecasting the severity of occupational injuries shall be all industries’ top priority. The use of machine learning is theoretically valuable to assist the predictive analysis, thus, this study attempts to propose a feature-optimized predictive model for anticipating occupational injury severity. A public database of 66,405 occupational injury records from OSHA is analyzed using five sets of machine learning models: Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, Decision Tree, and Random Forest. For model comparison, Random Forest outperformed other models with higher accuracy and F1-score. Therefore, it highlighted the potential of ensemble learning as a more accurate prediction model in the field of occupational injury. In constructing the model, this study also proposed the feature optimization technique that revealed the three most important features; ‘nature of injury’, ‘type of event’, and ‘affected body part’ in developing model. The accuracy of the Random Forest model was improved by 0.5% or 0.895 and 0.954 for the prediction of hospitalization and amputation, respectively by redeveloping and optimizing the model with hyperparameter tuning. The feature optimization is essential in providing insight knowledge to the Safety and Health Practitioners for future injury corrective and preventive strategies. This study has shown promising potential for smart workplace surveillance.
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Citation: Khairuddin, M.Z.F.; Lu Hui,
P.; Hasikin, K.; Abd Razak, N.A.; Lai,
K.W.; Mohd Saudi, A.S.; Ibrahim, S.S.
Occupational Injury Risk Mitigation:
Machine Learning Approach and
Feature Optimization for Smart
Workplace Surveillance. Int. J.
Environ. Res. Public Health 2022,19,
13962. https://doi.org/10.3390/
ijerph192113962
Academic Editors: Fatemeh Davoudi,
Steven Freeman, Gretchen Mosher
and Mack Shelley
Received: 20 September 2022
Accepted: 25 October 2022
Published: 27 October 2022
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4.0/).
International Journal of
Environmental Research
and Public Health
Article
Occupational Injury Risk Mitigation: Machine Learning Approach
and Feature Optimization for Smart Workplace Surveillance
Mohamed Zul Fadhli Khairuddin 1,2 , Puat Lu Hui 1, Khairunnisa Hasikin 1, 3,* , Nasrul Anuar Abd Razak 1,
Khin Wee Lai 1, Ahmad Shakir Mohd Saudi 4and Siti Salwa Ibrahim 5
1Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya,
Kuala Lumpur 50603, Malaysia
2Environmental Healthcare Section, Institute of Medical Science Technology, Universiti Kuala Lumpur,
Kajang 40300, Selangor, Malaysia
3Centre of Intelligent Systems for Emerging Technology (CISET), Faculty of Engineering, Universiti Malaya,
Kuala Lumpur 50603, Malaysia
4Centre of Water Engineering Technology, Water Energy Section, Malaysia France Institute, Universiti Kuala
Lumpur, Bangi 43650, Selangor, Malaysia
5Negeri Sembilan State Health Department, Seremban 70300, Negeri Sembilan, Malaysia
*Correspondence: khairunnisa@um.edu.my
Abstract:
Forecasting the severity of occupational injuries shall be all industries’ top priority. The use
of machine learning is theoretically valuable to assist the predictive analysis, thus, this study attempts
to propose a feature-optimized predictive model for anticipating occupational injury severity. A
public database of 66,405 occupational injury records from OSHA is analyzed using five sets of
machine learning models: Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, Decision
Tree, and Random Forest. For model comparison, Random Forest outperformed other models with
higher accuracy and F1-score. Therefore, it highlighted the potential of ensemble learning as a more
accurate prediction model in the field of occupational injury. In constructing the model, this study
also proposed the feature optimization technique that revealed the three most important features;
‘nature of injury’, ‘type of event’, and ‘affected body part’ in developing model. The accuracy of the
Random Forest model was improved by 0.5% or 0.895 and 0.954 for the prediction of hospitalization
and amputation, respectively by redeveloping and optimizing the model with hyperparameter
tuning. The feature optimization is essential in providing insight knowledge to the Safety and Health
Practitioners for future injury corrective and preventive strategies. This study has shown promising
potential for smart workplace surveillance.
Keywords:
artificial intelligence; machine learning; occupational injury; occupational safety and
health; features optimization
1. Introduction
According to International Labour Organization (ILO), 2.78 million workers died
from occupational injuries and around 374 million workers experienced non-fatal injuries,
annually from 2016 until 2019. Statistically, it is predicted that about 1000 workers will
be injured, meanwhile, 6500 workers will suffer from occupational diseases, and more
than 7500 workers will succumb as a consequence of various exposures to dangerous and
hazardous working environments [
1
]. Besides, workplace injury had resulted in a nearly 4%
loss of the world’s Gross Domestic Product (GDP) and the loss rose to 6% in certain nations
([
2
]. Additionally, occupational injuries and work-related diseases impact the companies’
operation in terms of reduction of the production process, shortage of skilled manpower,
and weakening the competitiveness, thus, reducing the productivity of the enterprises.
To some extent, these negative repercussions of occupational accidents may significantly
impact the entire community, extensively in the event of supply chain disruptions. Despite
Int. J. Environ. Res. Public Health 2022,19, 13962. https://doi.org/10.3390/ijerph192113962 https://www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2022,19, 13962 2 of 19
numerous countries’ persistent initiatives and policies to reduce the recurrence rate of
occupational injuries, the number of occupational accidents remains high [3].
Occupational accident statistics and data are valuable; therefore, it requires reliable
and robust techniques in extracting the information in the data for managing the causal
factors and generating the prediction patterns of occupational injury in more efficient
ways [
4
]. Among the linked cases of occupational accidents are those involving workers’
absences. In the event of a work-related injury, employees can take time off while being fully
compensated by their employers’ worker compensation program and medical expenditures.
As a result, machine learning algorithms have been deployed as a tool for optimizing and
reducing operating costs to increase operational efficiencies.
There are various techniques used to develop predictive models for occupational
injury outcomes, such as conventional statistical methods [
5
,
6
] and machine learning (ML)
approaches [
7
,
8
]. Presently, ML models are gaining popularity and their performance
prediction may outperform the conventional statistical methods due to the ability of ML
algorithms to process a large amount of raw data. These have initiated the emergence
of deep learning methods in predicting various outcomes, especially in the application
of medical and healthcare domains such as disease prediction [
9
,
10
], medical imaging
diagnosis [
11
,
12
], as well as the occupational accident outcomes [
13
]. In addition, ML
models are reliable techniques due to their potential capacities; (i) they can handle and
analyze large dimensional problems, (ii) it’s adaptable in reproducing the generation of data
regardless of the complexity of the data structure, and (iii) the promising ‘prognostic and
elucidative’ ability of ML, thereby, the application of ML models is compatible to forecast
the workplace accidents and injuries [
14
]. However, the exploration of these techniques in
forecasting occupational injury outcomes is still lacking and restricted [15].
There are few related studies applying ML techniques in analyzing occupational in-
juries; (i) Oyedele et al. in their study focused on the prediction of lost time injuries (LTI)
in the power transmission and distribution projects [
4
], (ii) Sarkar and Maiti [
3
] evaluated
the execution of ML models in the analysis of occupational accidents, (iii) Varghese et al.
demonstrated a thorough review on the risk of occupational injuries due to heat expo-
sure [
16
], and (iv) Noman et al. studied the potential of ML methods in the assessment of
occupational injuries among workers in Pakistan [
17
]. Other related works that utilized
ML techniques in predicting occupational injuries are summarized in Table 1.
Table 1. Summary of ML Models of Occupational Injury Prediction in Existing Literature.
References. Industry Input Variables ML Models Findings
[18] Construction
Age, sex, length of service, the
type of construction, employer
scale, and accident date.
LR, DT, RF, AdaBoost
RF is the best prediction
model with
the highest accuracy.
[19] Construction
Year, type of work, type of
accident, injured part,
assailing materials, and
cause of the accident.
SVM, Ensemble, PCA
SVM outperformed other
models with higher accuracy
in injury severity prediction.
[20] Mining
Sub-unit, classification,
accident type, occupation,
activity, injury source, nature
of the injury, injured
body part.
DT, RF, ANN ANN performed better than
all other models.
[21] Construction
15 variables: construction end
use, event type, part of the
body, cause of the accident
(human and environment),
and assigned tasks.
KNN, DT, RF
DT outperformed the other
techniques with better
sensitivity, recall, precision,
and F1 score.
Int. J. Environ. Res. Public Health 2022,19, 13962 3 of 19
Table 1. Cont.
References. Industry Input Variables ML Models Findings
[22] Construction
16 variables such as
organization and behavior,
technical management,
resources support,
management of the contract,
safety training, and
emergency management.
LR, DT, SVM,
NB, KNN, RF,
MLP, AutoML
NB and LR achieved good
performance in F1-Score and
AutoML is the best model to
predict the severity of
occupational injuries.
Note: Logistic Regression = LR, Decision Tree = DT, Random Forest = RF, Support Vector Machine = SVM,
Naïve Bayes = NB, K-Nearest Neighbor = KNN, Artificial Neural Network = ANN, Principal Component
Analysis = PCA, Multilayer Perceptron = MLP, AutoML = Automated Machine Learning.
Based on the previous related works, there are several types of ML methods used to
predict occupational injuries such as Decision Trees (DT) Random Forest (RF), Support
Vector Machine (SVM), Naïve Bayes (NB), Artificial Neural Network (ANN) and other
algorithms. Although these findings contributed additional value to the existing knowl-
edge, there is still a paucity of a comprehensive analysis of the use of ML in predicting
occupational injuries and the comparison of the performance prediction of different ML
models [
23
]. To address the inadequacies of the existing body of research, in terms of, most
of the previous studies focused only on a type of industry [
13
,
24
,
25
], therefore, limiting
the generalizability of the findings and inadequate exploration of important variables of
occupational injury as an example type of injury and prevalence of affected parts of the
body in model development [
26
]. Thus, there is a compelling need to propose a study to
support the overall review of the utilization of ML models in the prediction of occupational
injuries and to identify the best ML model in this research domain.
The motivation of this paper is to propose a predictive model of occupational injury
severity by comparing several contemporary ML techniques using the minimal factors
associated with occupational injury.
Overall, the main contributions of this study are as follows:
Firstly, most of the previous related studies focused only a type of industry, however,
this study differs as we analyzed a large occupational injury dataset encompassing a
wide range of industrial sectors. Incorporating industry-wide data on the severity of
occupational injuries into the development of the proposed model may close the gap
and enhance its generalizability.
Secondly, the abovementioned related studies in Table 1have utilized many input
features in producing a prediction model with higher accuracy. Though, our study
presented feature optimization techniques motivated by the ability of feature impor-
tance algorithms and hyperparameter optimization. We believed that the techniques
may enhance the development of the prediction model by reducing the amount of
data required for workplace injury prediction and classification.
Moreover, there are growing concerns from the previous research that emphasizes
the development of predictive analytics to help safety and health practitioners in
anticipating workplace accidents [
27
,
28
]. Therefore, this study’s findings will help the
International Labour Organization (ILO) and other human-resource-related govern-
ment sectors better comprehend the likelihood of workplace accidents and injuries,
as well as in the planning of workplace injury prevention strategies by safety and
health practitioners.
This paper is organized into 6 sections including, the introduction. The step-by-
step methodology is explained in Section 2; meanwhile, the findings of performance
prediction are presented in Section 3and further elaborated in Section 4. The conclusions
and recommendations for future research are in Section 5.
Int. J. Environ. Res. Public Health 2022,19, 13962 4 of 19
2. Materials and Methods
2.1. Dataset
The dataset used in this study was obtained from the United States, Occupational
Safety and Health Administration (OSHA, Washington, DC, USA) severe injury reports
(https://www.osha.gov/severeinjury), last accessed on 25 July 2022
2.2. Data Preparation
Categorical variables in this dataset are the type of industry, nature of the injury, part
of the affected body, type of event, type of source, hospitalization, and amputation. The
type of industry used the North American Industry Classification System (NAICS), and
20 categories were identified, such as agriculture, forestry, mining, and construction. The
nature of injury has 10 categories to describe the physical characteristics of the injury, for
example, surface wounds, traumatic injuries, and multiple disorders. Next, the part of
the affected body consists of 8 categories. Among them is the trunk, and upper and lower
extremities. The event or exposure categorized how the injury was inflicted. There are also
8 categories of events such as falls, slips, trips, and exposure to harmful substances. Last
but not least, there are 9 categories of source that describe the factors that caused the injury,
like tools, instruments, and machinery. These categories are pre-labeled according to the
Occupational Injury and Illness Classification Manual (OIICS).
Meanwhile, columns related to ID no, dates, employers’ addresses, city, state, latitude
and longitude were excluded. Other columns like inspection and secondary sources were
removed due to the majority of the entries containing ‘no value’. Also, any rows with
empty columns were eliminated. The textual narrative column is excluded as this study
aim to work on the structured data only.
In this study, only top labels by OIICS are utilized. For example, one of the top
labels for the type of event is contact with objects and equipment (E06) and within this
category, it expands into several sub-labels like needlestick (E61) and stuck by objects or
equipment (E62). Nonetheless, to prevent the scarce representation for each criterion [
29
],
only top labels are used for further analysis. Also, the non-classifiable class in part of the
affected body, type of event, and type of source are re-categorized into ‘Other(s)’. Overall,
a total of 66,405 structured data were used as the inputs in predicting the severity of the
occupational injury.
The data distributions of the utilized variables are shown in Table 2and Figure 1
illustrated the percentage of affected body parts of the occupational injuries from January
2015 until July 2021.
Table 2. Categorical variables and data distributions.
Variables Categories Distributions
Nature of Injury
N10 Traumatic injuries and disorders 2.3%
N11 Traumatic injuries to bones, nerves, spinal cord 31.9%
N12 Traumatic injuries to muscles, tendons, ligaments, joints
1.8%
N13 Open wounds 34.2%
N14 Surface wounds and bruises 1.1%
N15 Burns and corrosions 5.4%
N16 Intracranial injuries 3.6%
N17 Effects of environmental conditions 2.6%
N18 Multiple traumatic injuries and disorders 3.1%
N19 Other traumatic injuries and disorders 14%
Int. J. Environ. Res. Public Health 2022,19, 13962 5 of 19
Table 2. Cont.
Variables Categories Distributions
Type of Event
E01 Violence/other injuries by persons or animals 2.2%
E02 Transportation incidents 8.5%
E03 Fires and explosions 1.8%
E04 Falls, slips, trips 30.4%
E05 Exposure to harmful substances or environments 8.3%
E06 Contact with objects and equipment 46.3%
E07 Overexertion and bodily reaction 1.5%
E09 Other(s) 1%
Source of Injury
S01 Chemicals and chemical products 2.8%
S02 Containers, furniture, and fixtures 4.4%
S03 Machinery 25.5%
S04 Parts and materials 11.1%
S05 Persons, plants, animals, and minerals 4.3%
S06 Structures and surfaces 20.7%
S07 Tools, instruments, and equipment 8.9%
S08 Vehicles 14.1%
S09 Other(s) 8.2%
Type of Industry
I11 Agriculture, Forestry, Fishing and Hunting 1.8%
I21 Mining 2.9%
I22 Utilities 1.3%
I23 Construction 18%
I31 Manufacturing 33%
I42 Wholesale trade 5.6%
I44 Retail trade 7.4%
I48 Transportation and Warehousing 8.8%
I51 Information 1%
I52 Finance and Insurance 0.3%
I53 Real Estate Rental and Leasing 1%
I54 Professional, Scientific, and Technical Services 1.6%
I55 Management of Companies and Enterprises 0.1%
I56 Administrative/Waste Management
and Remediation 5.6%
I61 Educational Services 0.5%
I62 Health Care and Social Assistance 4.7%
I71 Arts, Entertainment and Recreation 1.3%
I72 Accommodation and Food Services 2%
I81 Other Services 1.9%
I92 Public Administration 1.2%
Hospitalization H1 Yes 80.6%
H0 No 19.4%
Amputation A1 Yes 26.4%
A0 No 73.6%
In executing this study, five categorical variables were chosen as the input for the
model development. There were (i) the type of industry, (ii) the affected body parts, (iii) the
nature of injury, (iv) the source of injury, and (v) the event of the injury. The target outcome
of the study is to predict the likelihood of occupational injury severity, whether the worker
is hospitalized or had an amputation.
2.3. Data Pre-Processing
Data pre-processing is an essential step in the development of machine learning
models. If the data collected comprises out-of-range values or missing values, it can
mislead the performance prediction of the models. In this study, a total of 295 (0.4%)
rows with empty columns were removed and the StandardScaler function was utilized for
data standardization.
Int. J. Environ. Res. Public Health 2022,19, 13962 6 of 19
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 6 of 19
Figure 1. Percentage of the affected body part(s).
In executing this study, five categorical variables were chosen as the input for the
model development. There were (i) the type of industry, (ii) the affected body parts, (iii)
the nature of injury, (iv) the source of injury, and (v) the event of the injury. The target
outcome of the study is to predict the likelihood of occupational injury severity, whether
the worker is hospitalized or had an amputation.
2.3. Data Pre-Processing
Data pre-processing is an essential step in the development of machine learning mod-
els. If the data collected comprises out-of-range values or missing values, it can mislead
the performance prediction of the models. In this study, a total of 295 (0.4%) rows with
empty columns were removed and the StandardScaler function was utilized for data
standardization.
2.4. Data Splitting
Then, the dataset is split into two sets: (i) the training set and (ii) the test set. In this
study, a 70:30 ratio, in which 70% of the data was the training set and 30% was used as the
test set. The 70:30 ratio is commonly used in various studies related to machine learning
classification, and this splitting ratio is believed to produce good accuracy and prevent
overfitting [30]. The flowchart of the proposed methodology is illustrated in Figure 2.
Figure 1. Percentage of the affected body part(s).
2.4. Data Splitting
Then, the dataset is split into two sets: (i) the training set and (ii) the test set. In this
study, a 70:30 ratio, in which 70% of the data was the training set and 30% was used as the
test set. The 70:30 ratio is commonly used in various studies related to machine learning
classification, and this splitting ratio is believed to produce good accuracy and prevent
overfitting [30]. The flowchart of the proposed methodology is illustrated in Figure 2.
2.5. Predictive Modeling
For the experimentation, five different machine learning algorithms were compared:
Support Vector Machine (SVM), Naïve Bayes (NB), K-Nearest Neighbors (KNN), Decision
Tree (DT), and an ensemble method, Random Forest (RF).
2.5.1. Support Vector Machine
SVM can generate the best generalizable decision boundaries for data classification.
In this algorithm, the original feature space is transformed into a space with a higher
dimension based on a kernel function defined by the operator. It then separates the two
classes with a hyperplane and optimizes support vectors to extend the margin between the
two classes. A hyperplane is defined as a boundary that separates the two categories. The
size of the hyperplane is determined by the number of input variables in the dataset [31].
2.5.2. Naïve Bayes
In the NB classifier, the input features of vector xare expected to be statistically
independent. It computes the conditional probability for each feature and then multiplies
them together. One of the advantages is the NB classifier can process large-scale and
high-dimensional data for prediction and classification tasks effectively. This classifier is
represented as:
p(ω|x1, . . . xn)=p(x1|ω)·p(x2|ω). . . p(x2|ω)p(ω)(1)
Int. J. Environ. Res. Public Health 2022,19, 13962 7 of 19
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 7 of 19
Figure 2. The overall research methodology.
2.5. Predictive Modeling
For the experimentation, five different machine learning algorithms were compared:
Support Vector Machine (SVM), Naïve Bayes (NB), K-Nearest Neighbors (KNN), Decision
Tree (DT), and an ensemble method, Random Forest (RF).
2.5.1. Support Vector Machine
SVM can generate the best generalizable decision boundaries for data classification. In
this algorithm, the original feature space is transformed into a space with a higher dimen-
sion based on a kernel function defined by the operator. It then separates the two classes
with a hyperplane and optimizes support vectors to extend the margin between the two
classes. A hyperplane is defined as a boundary that separates the two categories. The size of
the hyperplane is determined by the number of input variables in the dataset [31].
2.5.2. Naïve Bayes
In the NB classifier, the input features of vector x are expected to be statistically in-
dependent. It computes the conditional probability for each feature and then multiplies
them together. One of the advantages is the NB classifier can process large-scale and high-
dimensional data for prediction and classification tasks effectively. This classifier is rep-
resented as:
𝑝󰇛𝜔|𝑥,…𝑥
󰇜=𝑝
󰇛𝑥|𝜔󰇜∙𝑝
󰇛𝑥|𝜔󰇜…𝑝
󰇛𝑥|𝜔󰇜𝑝󰇛𝜔󰇜 (1)
Figure 2. The overall research methodology.
2.5.3. K-Nearest Neighbors
KNN is a method extensively used for data mining. The method will determine
the similarity between the new data and available data and group the new data into the
most similar categories to the existing data. The algorithm work by [
32
], (i) selecting the
number of K of the neighbors, (ii) computing the ‘Euclidean distance’, which is to measure
the distance between any two points. The formula as in Equation (2), and (iii) from the
calculation in (ii), the category for a new data point is assigned to the maximum number
of neighbors.
D = ((x2 x1)2+ (y2 y1)2) (2)
2.5.4. Decision Tree
The basic components in DT are as follows: (i) the root node is the initial point of the
DT model, (ii) the decision node is in charge of decision-making and extends the model
into multiple branches, and (iii) the leaf node is the outcome from those decisions [
33
]. The
classification in DT starts with the splitting of the root node into the leaf node. The splitting
continues until it reaches the leaf node. At each node, the classifier selects the feature and
corresponding feature threshold to execute a split. There is a maximum decrease in entropy
or impurity of the dataset after the split. When the leaf only contains samples from one
class, it is said to be the best-case scenario during the splitting process. To simplify the
process, in DT, the training dataset is processed by the classifier to generate a tree-like
decision structure, in which the starting point is a root node, and the finishing point is some
leaves. DT is commonly used in the prediction analysis of occupational accidents due to its
easier interpretability [34].
Int. J. Environ. Res. Public Health 2022,19, 13962 8 of 19
2.5.5. Random Forest
RF is an ensemble algorithm that uses bagging as the ensemble method and decision
trees as an individual method, thus helping to reduce variance and bias in improving
the findings [
11
,
13
]. The classifier collaborates several decision trees and a more robust
classifier with better generalization and easier to tune the hyperparameter to overcome
overfitting issues [
35
]. For classification tasks in RF, each tree provides a classification or
considers a ‘vote’. The forest then decides the classification with the majority of the ‘votes’
as illustrated in Figure 3.
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 8 of 19
2.5.3. K-Nearest Neighbors
KNN is a method extensively used for data mining. The method will determine the
similarity between the new data and available data and group the new data into the most
similar categories to the existing data. The algorithm work by [32], (i) selecting the number
of K of the neighbors, (ii) computing the ‘Euclidean distance’, which is to measure the
distance between any two points. The formula as in Equation (2), and (iii) from the calcu-
lation in (ii), the category for a new data point is assigned to the maximum number of
neighbors.
D = ((x2 x1)² + (y2 y1)²) (2)
2.5.4. Decision Tree
The basic components in DT are as follows: (i) the root node is the initial point of the
DT model, (ii) the decision node is in charge of decision-making and extends the model
into multiple branches, and (iii) the leaf node is the outcome from those decisions [33].
The classification in DT starts with the splitting of the root node into the leaf node. The
splitting continues until it reaches the leaf node. At each node, the classifier selects the
feature and corresponding feature threshold to execute a split. There is a maximum de-
crease in entropy or impurity of the dataset after the split. When the leaf only contains
samples from one class, it is said to be the best-case scenario during the splitting process.
To simplify the process, in DT, the training dataset is processed by the classifier to gener-
ate a tree-like decision structure, in which the starting point is a root node, and the finish-
ing point is some leaves. DT is commonly used in the prediction analysis of occupational
accidents due to its easier interpretability [34].
2.5.5. Random Forest
RF is an ensemble algorithm that uses bagging as the ensemble method and decision
trees as an individual method, thus helping to reduce variance and bias in improving the
findings [11,13]. The classifier collaborates several decision trees and a more robust clas-
sifier with better generalization and easier to tune the hyperparameter to overcome over-
fitting issues [35]. For classification tasks in RF, each tree provides a classification or con-
siders a ‘vote’. The forest then decides the classification with the majority of the ‘votes’ as
illustrated in Figure 3.
Figure 3. Random forest classifier.
Figure 3. Random forest classifier.
In this study, the models are developed and customized according to the following
configurations:
Naïve Bayes: GaussianNB()
Support Vector Machines: SVC (kernel = ‘rbf’, random_state = 0)
Decision Tree: DecisionTreeClassifier (criterion = ‘entropy’, random_state = 0)
K-Nearest Neighbors: KneighborsClassifier (n_neighbors = 5, metric = ‘minkowski’,
p= 2)
Random Forest: RandomForestClassifier (n_estimators = 50, criterion = ‘entropy’,
random_state = 0)
Then, the dataset was imported into the Python environment and the following Python
libraries were applied in this study:
Numpy (np) is a package for scientific computing and it has time-efficient array
processing capabilities [36].
Pandas (pd) is an important and powerful tool for data writing, data reading, data
analysis, and manipulation [37].
Matplotlib (plt) is a visualization package in python. It helps to create interactive
figures and informative visualization of data.
Sklearn is a robust library for machine learning, especially on predictive analysis. It
provides various tools including classification, preprocessing, clustering, regression,
and dimensionally reduction. These machine learning algorithms were developed in
the Sklearn of Python libraries.
Int. J. Environ. Res. Public Health 2022,19, 13962 9 of 19
2.6. Machine Learning Models Evaluation
A confusion matrix is a technique used for model evaluation, especially for classifi-
cation algorithms. It is visualized in a tabular way; each row represents an actual class,
meanwhile, a predicted class represents each column. From there, the counts on “True
Positive” (TP), “True Negative” (TN), “False Positive” (FP), and “False Negative” (FN) are
used to compute the performance metrics in assessing the models. Figure 4is the confusion
matrix. For example, in a prediction of hospitalization in this study, the definition of the
confusion matrix is as follows:
(TP) is the positive instances of injured workers that are actually hospitalized and
correctly predicted as hospitalized.
(FP) is the negative instances of injured workers that are un-hospitalized but wrongly
predicted as hospitalized.
(FN) is the positive instances of injured workers that are hospitalized but wrongly
predicted as un-hospitalized.
(TN) is the negative instances of injured workers that are actually un-hospitalized and
also, correctly predicted as un-hospitalized.
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 9 of 19
In this study, the models are developed and customized according to the following
configurations:
Naïve Bayes: GaussianNB()
Support Vector Machines: SVC (kernel = ‘rbf’, random_state = 0)
Decision Tree: DecisionTreeClassifier (criterion = ‘entropy’, random_state = 0)
K-Nearest Neighbors: KneighborsClassifier (n_neighbors = 5, metric = ‘minkowski’,
p = 2)
Random Forest: RandomForestClassifier (n_estimators = 50, criterion = ‘entropy’,
random_state = 0)
Then, the dataset was imported into the Python environment and the following Py-
thon libraries were applied in this study:
Numpy (np) is a package for scientific computing and it has time-efficient array pro-
cessing capabilities [36].
Pandas (pd) is an important and powerful tool for data writing, data reading, data
analysis, and manipulation [37].
Matplotlib (plt) is a visualization package in python. It helps to create interactive fig-
ures and informative visualization of data.
Sklearn is a robust library for machine learning, especially on predictive analysis. It
provides various tools including classification, preprocessing, clustering, regression,
and dimensionally reduction. These machine learning algorithms were developed in
the Sklearn of Python libraries.
2.6. Machine Learning Models Evaluation
A confusion matrix is a technique used for model evaluation, especially for classifi-
cation algorithms. It is visualized in a tabular way; each row represents an actual class,
meanwhile, a predicted class represents each column. From there, the counts on “True
Positive” (TP), “True Negative” (TN), False Positive (FP), and “False Negative” (FN)
are used to compute the performance metrics in assessing the models. Figure 4 is the con-
fusion matrix. For example, in a prediction of hospitalization in this study, the definition
of the confusion matrix is as follows:
(TP) is the positive instances of injured workers that are actually hospitalized and
correctly predicted as hospitalized.
(FP) is the negative instances of injured workers that are un-hospitalized but wrongly
predicted as hospitalized.
(FN) is the positive instances of injured workers that are hospitalized but wrongly
predicted as un-hospitalized.
(TN) is the negative instances of injured workers that are actually un-hospitalized
and also, correctly predicted as un-hospitalized.
Figure 4. Confusion Matrix.
Figure 4. Confusion Matrix.
Performance Metrics
Five performance metrics; accuracy, precision, recall, F1-score, and AUC value were
employed to understand and interpret the performance prediction of the machine learning
models. In each metric used in this experiment, the scores ranged from 0 to 1, in which a +1
score represents model perfection [29].
Accuracy is measuring the fraction of the total samples correctly classified. It is
expressed as “(TP + TN)/(TP + TN + FP + FN)”. For example, it is a ratio of cor-
rectly classified injured workers and hospitalized (TP + TN) to the total number of
injured workers.
Precision is the proportion of correctly classified injured workers and hospitalized to
the total workers predicted to be hospitalized. It is calculated by “(TP)/(TP + FP)”.
Recall or sensitivity is known as measuring the fraction of all positive samples that are
correctly predicted as positive and expressed as “(TP)/(TP + FN)”. In this study, it is a
ratio of the correctly classified injured workers and hospitalized divided by the total
number of injured workers and hospitalized.
F1-score is the “harmonic mean” of precision and recall. It is obtained by combining
precision and recall into a single measure and expressed as:
F1 score =2×Recall ×Precision
Recall +Precision (3)
Int. J. Environ. Res. Public Health 2022,19, 13962 10 of 19
Receiver Operator Characteristic (ROC) is extensively used to provide illustrative
information on the performance of the ML algorithms. It contains information on
a series of thresholds and is summarized in a single value by the ‘Area Under the
Curve’ (AUC)
2.7. Feature Optimization
The purpose of this phase is to assess and rank the most important attribute of
the occupational injury severity prediction model. Each ML model’s performance was
compared, and the model with the best results was utilized to derive the important feature
for occupational injury severity. The feature with the highest significance score is the most
significant predictor of the model. The steps to calculate the feature importance are further
elaborated in Section 3.3, depending on the best performance model algorithms.
The stage of feature optimization consisted of redeveloping the optimum performance
model using only the three most important features as the input variables. The model then
undergoes hyperparameter tuning, using the k-fold cross-validation technique. K-fold
cross-validation is a technique used to validate the effectiveness of a proposed prediction
model. The steps of how k-fold works are as follows: First, a dataset is split into a
k
number
of folds. In the first iteration, Fold 1 is used as a testing set and the other folds, such as Fold
2, 3, 4,
. . .
,Kas the training set. In the second iteration, Fold 2 is the test set; meanwhile,
the remaining folds are the training set. This process remains until each fold has been used
once, as a test set. In this cross-validation, each entry is served for validation for one time
in the whole process [38].
This study employed a k-value of 10 with a number of iterations of 100, in optimizing
the proposed model. The use of k = 10 is common in the applied ML model, as its practicality
reduces a test error rate from higher bias or variance [
39
]. Theoretically, the difference in
size between the training set and the re-sampling subsets will decrease as the k increases.
Concurrently, the bias of the techniques is lesser as this difference becomes smaller [
40
].
Afterward, the cross-validation accuracy scores are computed for all hyperparameter
combinations. The average cross-validation accuracy score will then be compared with
the initial model with 5-feature inputs. Finally, the model with the best accuracy score is
chosen as the final model. To note, in sklearn, this stage is conveniently handled by the
RandomizedSearch CV method.
The proposed step-by-step feature optimization process is illustrated in Figure 5.
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 11 of 19
Figure 5. Feature optimization steps.
3. Results
This section is aimed to forecast the severity of occupational injuries in terms of the
possibility of hospitalization and amputation. Five ML models, including SVM, KNN, NB,
DT, and RF, were executed to develop the predictive systems. The binary classification
was involved in the predictive system as the outcome variables are consists of two classes
only, either Yes or No.
3.1. Performance Prediction
The performance prediction of each ML algorithm was analyzed and compared to
select the best-employed model for the prediction of occupational injury severity. The
findings are in two parts; the first part is the comparison of the prediction performance of
SVM, KNN, NB, DT, and RF in predicting the likelihood of hospitalization, and the second
part is the comparison of these models in predicting the likelihood of amputation.
3.1.1. Hospitalization
In predicting the likelihood of hospitalization, all ML models used in this study had
promising performances in each metric; accuracy, precision, recall, F1-score, and AUC. Spe-
cifically, RF had the best overall accuracy score of 0.89. In terms of the F1-score, RF also
achieved the best performance of 0.928, similar to the DT model. Next, the ML algorithm
with the highest precision was NB with 0.985, followed by SVM (0.984) and DT model with
0.98. For recall, the KNN model received the best score of 0.895. Meanwhile, the AUC value
for all ML models showed significant performance ranges from 0.86 to 0.91.
For the prediction of the likelihood of hospitalization, RF was suggested as the best
performance as the model achieved the highest accuracy and F1-score and the least per-
formance model was NB as the model recorded the lowest score for accuracy, recall, and
F1-score as compared to other algorithms. Table 3 shows the overall accuracy, precision,
recall, F1-score, and AUC of the ML algorithms for the prediction of hospitalization.
Figure 5. Feature optimization steps.
Int. J. Environ. Res. Public Health 2022,19, 13962 11 of 19
3. Results
This section is aimed to forecast the severity of occupational injuries in terms of the
possibility of hospitalization and amputation. Five ML models, including SVM, KNN, NB,
DT, and RF, were executed to develop the predictive systems. The binary classification was
involved in the predictive system as the outcome variables are consists of two classes only,
either Yes or No.
3.1. Performance Prediction
The performance prediction of each ML algorithm was analyzed and compared to
select the best-employed model for the prediction of occupational injury severity. The
findings are in two parts; the first part is the comparison of the prediction performance of
SVM, KNN, NB, DT, and RF in predicting the likelihood of hospitalization, and the second
part is the comparison of these models in predicting the likelihood of amputation.
3.1.1. Hospitalization
In predicting the likelihood of hospitalization, all ML models used in this study had
promising performances in each metric; accuracy, precision, recall, F1-score, and AUC.
Specifically, RF had the best overall accuracy score of 0.89. In terms of the F1-score, RF also
achieved the best performance of 0.928, similar to the DT model. Next, the ML algorithm
with the highest precision was NB with 0.985, followed by SVM (0.984) and DT model with
0.98. For recall, the KNN model received the best score of 0.895. Meanwhile, the AUC value
for all ML models showed significant performance ranges from 0.86 to 0.91.
For the prediction of the likelihood of hospitalization, RF was suggested as the best
performance as the model achieved the highest accuracy and F1-score and the least per-
formance model was NB as the model recorded the lowest score for accuracy, recall, and
F1-score as compared to other algorithms. Table 3shows the overall accuracy, precision,
recall, F1-score, and AUC of the ML algorithms for the prediction of hospitalization.
Table 3. Performance Prediction of all ML Models for Hospitalization.
ML Models Accuracy Precision Recall F1-score AUC
KNN 0.883 0.957 0.895 0.925 0.86
DT 0.880 0.980 0.881 0.928 0.90
NB 0.879 0.985 0.862 0.920 0.91
SVM 0.884 0.984 0.870 0.924 0.91
RF 0.890 0.978 0.883 0.928 0.90
Note: Bold indicates the highest value on each performance metric.
3.1.2. Amputation
Next, for the prediction of an amputation, RF showed the highest accuracy score
(0.949), DT was the best precision (0.861), and the SVM model achieved the best recall
(0.967) among these five algorithms. In addition, for the F1 score, RF achieved the highest
score of 0.909, followed by DT and KNN with 0.907 and 0.902, respectively. All models had
an AUC value of 0.95, except the NB model of 0.94.
In terms of predicting the severity of an amputation, RF was indicated as the best and
most reliable model compared to the other algorithms. RF had outperformed other models
as it achieved the highest score in accuracy and F1-score, as well as generated consistent
scores in precision, recall, and AUC value. Nevertheless, the NB model was considered the
poor performance model for this prediction, as it scored the lowest for accuracy, precision,
recall, F1-score, and AUC value. Table 4displays the overall performance of accuracy,
precision, recall, F1-score, and AUC for the prediction of an amputation.
Int. J. Environ. Res. Public Health 2022,19, 13962 12 of 19
Table 4. Performance Prediction of all ML Models for Amputation.
ML Models Accuracy Precision Recall F1-Score AUC
KNN 0.945 0.869 0.948 0.902 0.95
DT 0.948 0.861 0.959 0.907 0.95
NB 0.934 0.831 0.942 0.883 0.94
SVM 0.939 0.831 0.967 0.894 0.95
RF 0.949 0.860 0.963 0.909 0.95
Note: Bold indicates the highest value on each performance metric.
3.2. Performance Comparison
As the overall accuracy result is the most frequently used performance measure for
classification tasks [
41
43
], the accuracy score of each ML model in both injury severity
prediction, hospitalization, and amputation, are compared in determining the best predic-
tion model for this study. It is verified that RF comparatively performs well in accuracy as
compared to SVM, NB, KNN, and DT models. A pictorial representation of the accuracy
performance of each model is depicted in Figure 6.
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 13 of 19
Figure 6. Accuracy Comparison of all ML models; K-Nearest Neighbors (KNN), Decision Tree
(DT), Naïve Bayes (NB), Support Vector Machine (SVM), Random Forest (RF)
3.3. Feature Optimization
RF, as the best performing model, is utilized to analyze the important variables in
predicting the severity of occupational injury. Attributes with the highest importance
value are justified as the most significant contributor to the developed model. The calcu-
lation of feature importance through a random forest algorithm is done by the following
steps [44];
(1) The individual nodes’ importance per tree is calculated using the formula in Equa-
tion (4), where
𝑛𝑖
importance of node j,
𝑤
=
weighted samples reaching node j
and
𝐶
= impurity value of the node.
𝑛𝑖
𝑤
𝐶
𝑤󰇛󰇜𝐶󰇛󰇜
𝑤󰇛󰇜
𝐶󰇛󰇜 (4)
(2) After the nodes’ importance is calculated, the feature importance per tree is deter-
mined through Equation (5).
𝑓
𝑖=𝑛𝑖:   
𝑛𝑖  
(5)
(3) The calculation is normalized as per Equation (6) to a value from 0 to +1.
𝑛𝑜𝑟𝑚𝑓𝑖=
𝑓
𝑖
𝑓
𝑖  
(6)
(4) The calculation from (3) is averaged across the entire forest and divided by total trees
by using Equation (7).
𝑅𝐹𝑓𝑖=𝑛𝑜𝑟𝑚
𝑓
𝑖  
T (7)
(5) The final value is arranged in descending order, in which the most important feature
appears in the first rank. The higher the value, the more important the feature.
For this experiment, the feature importance values revealed the ‘nature of injury’ as
the most important variable, followed by ‘type of event’ and ‘affected body part’. The
calculated values for feature importance are shown in Table 5.
Figure 6.
Accuracy Comparison of all ML models; K-Nearest Neighbors (KNN), Decision Tree (DT),
Naïve Bayes (NB), Support Vector Machine (SVM), Random Forest (RF).
3.3. Feature Optimization
RF, as the best performing model, is utilized to analyze the important variables in
predicting the severity of occupational injury. Attributes with the highest importance value
are justified as the most significant contributor to the developed model. The calculation of
feature importance through a random forest algorithm is done by the following steps [
44
];
(1) The individual nodes’ importance per tree is calculated using the formula in
Equation (4)
,
where
nij=
importance of node j,
wj
= weighted samples reaching node j and
Cj= impurity value of the node.
nij=wjCjwl e f t(j)Cle f t(j)wr ight(j)Cright(j)(4)
Int. J. Environ. Res. Public Health 2022,19, 13962 13 of 19
(2)
After the nodes’ importance is calculated, the feature importance per tree is deter-
mined through Equation (5).
f ii=j:node j spl its on f eature i nij
kall nodes nik
(5)
(3)
The calculation is normalized as per Equation (6) to a value from 0 to +1.
nor m f ii=f ii
jall f e atures f ij
(6)
(4)
The calculation from (3) is averaged across the entire forest and divided by total trees
by using Equation (7).
RF f i i=jall trees norm f i ij
T(7)
(5)
The final value is arranged in descending order, in which the most important feature
appears in the first rank. The higher the value, the more important the feature.
For this experiment, the feature importance values revealed the ‘nature of injury’ as
the most important variable, followed by ‘type of event’ and ‘affected body part’. The
calculated values for feature importance are shown in Table 5.
Table 5. Feature Importance based on Ranking.
Feature Importance Value Description
Nature of Injury 0.406367 Identifies the main physical characteristic(s)
of the occupational injury.
Type of Event 0.254030 Identifies how the occupational
injury was produced.
Affected Body Part(s) 0.243115 Identifies the part of the body affected
by the nature of the occupational injury.
Source of Injury 0.064195
Identifies the workplace factors such as
objects, substances, equipment, and other
external factors that were responsible
for the occupational injury.
Type of Industry 0.032293 Identifies the nature of the organization,
company, or enterprise
Based on this finding, the model experimentation using the RF algorithm was re-
developed by eliminating the 2 least important features; ‘source of injury’ and ‘type of
industry’. After hyperparameter tuning, we have identified that the accuracy score of
the optimized RF model was improved to 0.5%, 0.895 and 0.954 for both predictions,
respectively. The cross-validation scores and hyperparameter tuning performances are
presented in Tables 6and 7.
Table 6. Cross-validation Scores.
Prediction Criteria Value
Hospitalization Mean 0.893
SD 0.0029
Amputation Mean 0.949
SD 0.0028
Note: SD = Standard Deviation.
Int. J. Environ. Res. Public Health 2022,19, 13962 14 of 19
Table 7. Hyperparameter Tuning Performances.
Prediction Criteria Value
Hospitalization Overall Accuracy 0.895
Amputation 0.954
Hospitalization
Amputation Optimized Parameters
‘n_estimators’: 1200,
‘min_samples_split’: 15,
‘min_samples_leaf’: 10,
‘max_features’: ‘sqrt’,
‘max_depth’: 15
4. Discussion
In general, our study shows that the RF model has performed better than other
classifiers in terms of its accuracy. The finding is in agreement with the study by [
18
] where
RF attained an accuracy of 91.98% in classifying employers with higher fatality risk at
construction sites. It is proven in their study that RF outperformed LR, DT, and AdaBoost.
In another study, [
45
] preferred the RF technique in predicting industrial accidents, which
gave them an accuracy score of 79%. Next, the RF algorithm was executed in predicting the
type of occupational accidents during construction [
46
]. Interestingly, their study was able
to integrate the environmental data and occupational accident inputs in developing a model
with 71.3% accuracy. On the other hand, the RF was observed as the most functioning
model in the multi-class classification task of predicting occupational injuries including the
prediction of causal factors of occupational injuries [47].
By the findings, this paper strongly recommended the ensemble method as the ma-
chine learning technique of choice, as it has demonstrated more accuracy in predicting the
severity of occupational injuries. The utilization of the ensemble method in the predictive
analysis is beneficial, as it collaborates the prediction of several classifiers by combining a
series of weak classifiers into a single stronger classifier, thus enhancing the performance
prediction. As the RF model follows the ‘majority votes decision rule’, the combination of
these results will give a good generalization, therefore, resulting in higher accuracy. In terms
of the F1-score, the RF model gained the highest as the model obtained higher precision
and recall, as well. In principle, the higher the precision and recall, the higher the F1-score;
and the higher the F1-score, the more robust the classifier [
48
]. Since the usage of RF-based
ensemble learning is relatively limited and lacking in occupational injury studies [
49
], the
findings are believed to support the ensemble of trees in providing more efficiency and
accuracy in performance prediction, especially for data classification problems.
In addition, the feature importance was assessed to identify the significant factors
related to occupational injury. Feature importance is considered the most current strategy
for assisting ML model developers to comprehend and interpret their models. Most notably,
this technique is critical in providing the classification tasks with insight knowledge [50].
In this study, an RF algorithm was employed to quantify the relevance of features.
As supported by [
50
], the RF model, as compared to Logistic Regression, proven better
in explaining the feature importance in the classification models. This research has iden-
tified the ‘nature of injury’ as the most influential variable in the dataset. This variable
was deemed significant by [
20
,
31
] in the mining and agriculture industries, respectively.
The most prevalent types of nature of injury in the dataset were ‘open wound’ and ‘trau-
matic injuries to bones, nerves, spinal cord’. Next, the ‘type of event or exposure’ was
the second most important feature. It is interesting to note that ‘contact with objects or
equipment’ and ‘falls, slips, trips’ were the highest reported event or exposure that resulted
in occupational injury.
By revealing these significant variables, it will be beneficial to the top management to
design and systematically improve their ‘Workplace Injury Control Plan’ such as addressing
appropriate work-safety training programs, providing adequate engineering control and
personal protective equipment, as well as, maintaining the housekeeping and hygiene of
the workplace environment in reducing the accident cases and lessening the severity of
Int. J. Environ. Res. Public Health 2022,19, 13962 15 of 19
workplace injuries. For instance, if workers are required to handle machines, and their
upper extremities are exposed, it is recommended that they be given adequate personal
protective equipment and instructed on the safe operating methods for handling the
machines. In addition, if employees have fallen or slipped on the job and sustained injuries
to their lower extremities, frequent workplace audits and inspections, as well as proper
housekeeping, are the recommended control measures.
It is important for the models to not only anticipate the severity of the occupational
injury of a worker but also to include these variables, particularly the nature of the injury
and how the workers were exposed into the justification that are; comprehensible and
quantifiable. As indicated in Figure 7, this will enhance the models to provide the perfect
future strategies for corrective and preventive measures for Safety and Health Practitioners.
The applicability of a predictive model or system will be determined by the elaboration of
what has to be changed for improvement and the foresight of the potential hazards and
risks of workplace injuries [51].
Figure 7.
The proposed framework of AI-assisted occupational safety and health at workplace management.
In addition, the focus of this paper’s feature importance selection is to support the
burgeoning field of study known as “Explainable Artificial Intelligence” (XAI). XAI is the
technique used to explain ML predictions and aid in decision-making, particularly when
these approaches are implemented in high-criticality sectors, such as medical and personal
health applications [
52
]. In the field of occupational injuries, it is evident that worker safety,
health and well-being are of the utmost importance. Detecting the severity of occupational
injuries is crucial to the recovery or rehabilitation phases, as well as the injured workers’
successful return to work. Therefore, it is necessary to construct model predictions and
conclusions that are explicable and interpretable to justify their trustworthiness.
Lastly, our investigation validated the need for feature optimization procedures in
developing an accurate prediction model. This technique has been shown to be able to
choose the most significant variables connected to the desired outcomes and eliminate fewer
important variables, hence enhancing the capability of developing a high accuracy and
precise prediction model using fewer variables. It is considered that prediction models with
fewer variables are favored, as compared to the models with a large set of variables [
53
].
This is because the simpler model will ease the practitioners and operators in the field to
Int. J. Environ. Res. Public Health 2022,19, 13962 16 of 19
interpret and implement in their practices [
54
]. On the contrary, the use of many variables
is impractical due to the following reasons; (i) a large set of variables, commonly has a
‘negligible effect’ on the target outcomes [
53
], (ii) in terms of practicality, many variables
tend to increase the computational tasks and complexity of the model development [
40
],
and (iii) more variables in the model make the model highly dependent on the ‘observed
data’, however, the data is most likely unavailable and difficult to collect. According to
that, this study can highlight feature optimization as a useful technique in selecting fewer
important variables in developing a prediction model with higher accuracy.
Despite producing promising performance prediction results, this study has some
limitations. First, the dataset contains no socio-demographic information about the affected
employees. Therefore, the investigation of the role of age, gender, and years of experience,
including the job position, is restricted in this work. Next, it shall include additional
Occupational Safety and Health (OSH) analytic data such as safety and health audit reports,
medical information such as days off until return to work, and risk assessment results to
improve the predictive capabilities of the models.
5. Conclusions
In this research, we demonstrate the execution of five sets of ML algorithms: SVM,
KNN, NB, DT, and RF in classifying the occupational injury severity. We find that these
techniques provide satisfying performances to the predicted classes, hospitalization or am-
putation. For both predictions, the RF model consistently outperformed other ML models
with higher accuracy, F1-score, and AUC value. Consequently, this finding is essential in
highlighting the potential of the ensemble learning method as a better prediction model.
For feature optimization, it has revealed the ‘nature of injury’, ‘type of event’ and ‘af-
fected body part’ as the three most significant factors behind the prediction of occupational
injury severity. After hyperparameter tuning, the accuracy of the optimized
RF model
is
improved to 0.895 and 0.954 for both predictions, respectively. This information is ben-
eficial for the Safety and Health Managers to continuously improve their Occupational
Safety and Health Management System (OSHMS) especially in reducing workplace injury
cases. Finally, this study has employed a broad section of industrial sectors as the input
classification. The integration of various industries’ information may improve the gener-
alizability of the prediction model. To the best of the author’s knowledge, this study has
provided the latest baseline findings in the prospect of utilizing the feature importance and
hyperparameter optimization in the prediction of occupational injury severity. In addition,
the findings revealed the predictive ability of the proposed model is improved.
The goal of this research field is to incorporate the unlabeled data from the occupational
injury report such as text narratives and injury images with the labeled data to improve the
predictive capabilities of the model [
55
]. As suggested by [
56
], the use of the deep learning
method using the Generative Adversarial Network (GAN) can assist in medical diagnosis
with full utilization of labeled and unlabeled data. Moreover, [
57
] used the GAN-driven
approach and was able to predict the patient’s length of hospitalization in managing the
health resources. In relation to the occupational safety domain, [
20
] has proposed the GAN
technique to overcome the data imbalance issues in the occupational injury report, and to
investigate alternative forms of deep neural architecture.
Author Contributions:
Conceptualization, M.Z.F.K., K.H. and N.A.A.R.; Data curation, M.Z.F.K.,
P.L.H. and K.H.; Formal analysis, M.Z.F.K. and P.L.H.; Methodology, M.Z.F.K. and P.L.H.; Soft-
ware, M.Z.F.K. and A.S.M.S.; Supervision, K.H. and N.A.A.R.; Validation, K.H., N.A.A.R. and
K.W.L.;
Visualization
, K.H. and S.S.I.; Writing—original draft, M.Z.F.K.; Writing—review and editing,
P.L.H., K.H., N.A.A.R. and K.W.L. All authors have read and agreed to the published version of
the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Int. J. Environ. Res. Public Health 2022,19, 13962 17 of 19
Data Availability Statement:
The link to publicly archived datasets for this study: https://www.
osha.gov/severeinjury, last accessed on 25 July 2022.
Conflicts of Interest: The authors declare no conflict of interest.
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