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An evolution based hybrid approach for heart diseases classification and
associated risk factors identification.
Saman Iftikhar1, Kiran Fatima2, Amjad Rehman1, Abdulaziz S Almazyad4, Tanzila Saba3*
1College of Computer and Information Systems, Al-Yamamah University, Riyadh, 11512, Saudi Arabia
2Departent of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan
3College of Computer and Information Sciences, Prince Sultan University Riyadh, 11586 Saudi Arabia
4College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia
Abstract
With the advent of voluminous medical database, healthcare analytics in big data have become a major
research area. Healthcare analytics are playing an important role in big data analysis issues by
predicting valuable information through data mining and machine learning techniques. This prediction
helps physicians in making right decisions for successful diagnosis and prognosis of various diseases. In
this paper, an evolution based hybrid methodology is used to develop a healthcare analytic model
exploiting data mining and machine learning algorithms Support Vector Machine (SVM), Genetic
Algorithm (GA) and Particle Swarm Optimization (PSO). The proposed model may assist physicians to
diagnose various types of heart diseases and to identify the associated risk factors with high accuracy.
The developed model is evaluated with the results reported by the literature algorithms in diagnosing
heart diseases by taking the case study of Cleveland heart disease database. A great prospective of
conducting this research is to diagnose any disease in less time with less number of factors or symptoms.
The proposed healthcare analytic model is capable of reducing the search space significantly while
analyzing the big data, therefore less number of computing resources will be consumed.
Keywords: Healthcare analytics, Optimization algorithms, Heart diseases classification.
Accepted on December 15, 2016
Introduction
Healthcare information systems are becoming important as
they develop relational databases enriched with valuable
informative medical data [1-3]. Now-a-days, data mining and
machine learning algorithms are being used for analyzing and
predicting these huge volume databases. Clinical decision
support systems are developed as a result of these algorithms
based methodologies. In this way, the risks associated with
incorrect diagnostic decisions and the cost associated with
wrong clinical medication of patients will be minimized [4,5].
The primary goal of healthcare analytics is to develop different
predictive models in various medical domains like heart
diseases, cancers, diabetes and other complex diseases [6-8].
Different data mining techniques are being used in healthcare
informatics such as: supervised learning technique,
unsupervised learning technique and Feature selection
techniques [9,10]. These techniques are extensively used in
numerous real world applications [11,12]. In supervised
learning technique such as classification technique, a model is
developed to classify different data classes and then this model
is used to predict the class of a feature whose class label is
unknown [13]. Training and testing data sets are used to
classify the objects in different classes. Another data mining
technique known as clustering is an unsupervised learning
technique which forms clusters of data objects with unknown
class labels on account of some similarity measure [14]. The
formed clusters are then used to derive association rules which
guide the classification of test instances. Feature selection is a
data mining technique used to select a subset of attributes that
considered to be most fit for making an optimal diagnosis
decision based on some selection criteria.
There are various algorithms in use for supervised learning
techniques and unsupervised learning technique. These
algorithms are being applied in healthcare analytics [15] with
more or less efficacy, such as Support Vector Machines (SVM),
Artificial Neural Networks (ANN), decision trees, Bayesian
networks, Support Feature Machines (SFM) and regression
analysis. This research presents a hybrid approach using a
supervised learning model based on a well known classifier
SVM and evolutionary optimization techniques (Genetic
Algorithm (GA) and Particle Swarm Optimization (PSO)
[16,17]. The results are evaluated with algorithms reported in
literature and have shown considerably improved accuracy of
ISSN 0970-938X
www.biomedres.info
Biomed Res- India 2017 Volume 28 Issue 8 3451
Biomedical Research 2017; 28 (8): 3451-3455
more than 88%. By using the proposed model for the
classification and diagnosis of a disease, big data analysis
issues may be resolved to a great extent. This paper is
organized in five sections; state of art is presented in section 2,
proposed methodology is presented in section 3, experiments
and results are discussed in section 4 and conclusion in section
5.
Proposed Methodology
Data collection
The data is collected from heart disease databases available at
UCI machine learning data set online repository as the
Cleveland heart disease patient's datasets. The data mining
problem to be solved is a multiclass problem. The dataset is
being segregated into five classes, such as: 0 corresponding to
absence of heart disease and 1,2,3,4 corresponding to four
different types of heart diseases (Acute Myocardial Infarction
(AMI), Percutaneous Coronary Intervention (PCI),
Percutaneous Tran luminal Coronary Angioplasty (PTCA) and
Coronary Artery Bypass Graft (CABG)). The most imperative
13 attributes and class label are given in Table 1.
Table 1. Data set attributes.
Features Description Value
age Age 16-80
sex Gender 1: Male
0 : Female
cp Chest Pain Type 1: typical angina
2: typical type angina
3: non-angina pain
4: asymptomatic
trestbps Trest Blood Pressure mm Hg on admission to the
hospital
chol Serum Cholesterol (mg/dl)
fbs Fasting Blood Sugar 0: <120 mg/dl
1: >120 mg/dl
restecg Resting electrographic
results
0: normal
1: having ST-T wave
abnormality
2: showing probable or
definite left ventricular
hypertrophy
thalach Maximum heart rate
achieved
exang Exercise induced angina 0 = no
1 = yes
oldpeak St depression induced by
exercise relative to rest
Slope Slope of the peak exercise
ST segment
1: unsloping
2: flat
3: downsloping
Ca Number of major vessels
colored by floursopy
0–3
thal 3: normal
6: fixed defect
7: reversible defect
Num Predicted attribute 0,1,2,3,4
Heart diseases classification through SVM
SVMs build a separating hyperplane represented linearly in
space of training points. The decision function for classifying
linearly separable and non-separable data points with respect to
the optimal hyperplane is shown in Figure 1. In this research,
experiments are performed to use SVM classifier with three
kernel functions and three methods for finding separating
hyperplane. Following are the steps of implementation for
heart diseases classification through SVM.
1. Load the data set in variable 'data' acquired from UCI
repository
2. Extract the classes in variable ' labels ' mentioned as 0 (no
disease) and 1,2,3,4 (other types of heart diseases)
3. Perform training and test sets division through 10-fold
cross validation and save the resulting train and test sets in
variables 'train' and 'test'
4. Build the SVM structure through Matlab Bioinformatics
toolbox builtin function 'svmtrain'
5. SVMStruct =
svmtrain(data(train,:),labels(train),'Kernel_Function',
kernel_func, 'Method', hyper_method, 'boxconstraint',bx)
6. Classify the test data instances through built in function
'svmclassify' svmclassify(SVMStruct,data(test,:))
7. Evaluate the classifier performance in terms of correct rate
(accuracy)
Heart diseases classification through GA-SVM
Genetic Algorithm (GA) is a computational model inspired by
evolution and has a basic motivation from the laws of natural
selection and genetics. This algorithm preserves significant
information through the repetitive application of genetic
operations on evolved individuals. In this way, the evolutionary
algorithm selects “N best features for classification out of total
M features”. Following main steps are performed to implement
GA in combination with SVM for heart diseases classification.
1. Generate initial random population of binary strings
(chromosomes) to represent presence (1) or absence (0) of
features
2. Perform genotype to phenotype conversion of all the
chromosomes to get desired features set
3. Get fitness value (classification accuracy) of random
individuals using phenotypes through SVM
4. Perform genetic operation 'tournament selection' to select
parent(s) individuals based on fitness value for application of
recombination operators crossover and mutation
Iftikhar/Fatima/Rehman/Almazyad/Saba
3452 Biomed Res- India 2017 Volume 28 Issue 8
5. Apply one point crossover and bit-wise uniform mutation on
selected parents individuals
6. Remove repetitive individuals from evolved population
7. Repeat step 2 to step 6 till stagnation (no improvement in
fitness value) for a specified number of generations
8. Return optimal feature subset with highest fitness value
Figure. 1. Maximum margin and optimal hyperplane of linear and non-linear SVM.
Heart diseases classification through PSO-SVM
Particle Swarm Optimization (PSO) algorithm is another
population-based optimization algorithm inspired from the
social behavior of bird flocking or fish schooling. PSO is
widely used in artificial intelligence applications for solving
different optimization problems. In PSO, all the potential
candidate solutions, called particles, trace the current optimum
particles and move through the search space in order to find the
global best solution.
From the heart diseases data set, each particle (data point) is
taken as a point in N dimensional problem space. Every
particle holds a position in terms of its coordinates in search
space which is to be updated with a velocity. Each particle's
position and velocity is updated in coordination with the best
particle based on fitness value (classification accuracy) that has
attained through SVM so far by that particle.
Experiments and Results
Parameters setting
In this research, the parameters of SVM kernel functions are
tuned in order to get optimal results. Three kernel functions
(Linear, Polynomial and Gaussian Radial Basis Function-RBF)
of SVM classifier are explored. Three methods for finding the
separating hyperplane of SVM are explored including
Quadratic Programming (QP) algorithm, Least Square (LS)
algorithm and Sequential Minimal Optimization (SMO)
algorithm. The values of cost parameter or box constraint,
order of polynomial function and RBF scaling parameter sigma
are found through grid search algorithm.
The parameters setting used for GA in our experiments is as
follows:
No. of Generations = 50, Population Size = 50, Chromosome
Length = 13,
Selection mechanism - Tournament (size = 7), Mutation rate =
0.01, Crossover rate = 0.8.
The parameters setting used for PSO algorithm in our
experiments is as follows:
No. of generations = 50, Population Size = 50, Particle Length
= 13.
Classification results
In this research, a number of experiments are performed in
order to explore the optimal combination of SVM parameters
in simple mode and in hybrid mode with GA and PSO. The
four test scenarios with highest classification results are only
given and explained.
In first two scenarios, SVM is explored in simple mode for
classification without feature selection.
Scenario 1: SVM with Least Square hyperplane finding
method and Linear kernel function (SVM-LS-Linear) using 10-
fold cross validation for training and testing divisions achieved
mean accuracy of 87.56% over 5 States of heart disease by
using 13 attributes (symptoms).
• Mean Accuracy over 5 States: 87.56%
For type 0 - Individual Accuracy: 83.17%
For type 1 - Individual Accuracy: 81.84%
For type 2 - Individual Accuracy: 88.23%
For type 3 - Individual Accuracy: 88.89%
For type 4 - Individual Accuracy: 95.64%
A hybrid approach for heart diseases classification and associated risk factors identification
Biomed Res- India 2017 Volume 28 Issue 8 3453
Scenario 2: SVM with Sequential Minimal
Optimization hyperplane finding method and RBF kernel
function (SVM-SMO-RBF) having = 12 using 10-fold cross
validation for training and testing divisions has achieved mean
accuracy of 87.95% over 5 States of heart disease by using 13
attributes (symptoms).
• Mean Accuracy over 5 States: 87.95%
For type 0 - Individual Accuracy: 85.83%
For type 1 - Individual Accuracy: 81.84%
For type 2 - Individual Accuracy: 88.24%
For type 3 - Individual Accuracy: 88.23%
For type 4 - Individual Accuracy: 95.63%
In other two scenarios, SVM is explored in hybrid mode for
classification with feature selection using GA and PSO
algorithm.
Figure 2. Classification Scores obtained through GA-SVM over 50
generations.
Figure 3. Detail of Fitness Values obtained through GA-SVM over 50
generations.
Scenario 3: The GA is used for optimal feature selection
where the quality (fitness) of a feature set is evaluated through
SVM classifier. The highest classification results are obtained
when SVM is used with RBF kernel function (=12) and SMO
hyperplane calculation method. The mean accuracy over 5
States achieved through GA and SVM hybrid approach is
88.10%.
The experiment with GA has shown the following scores
(classification accuracy) for 50 generations as in Figure 2
where the 31st generation has given the best fitness value. The
detailed graphs of GA individuals and fitness values obtained
through GA-SVM over 50 generations as shown in Figure 3.
The final chromosome of GA representing the best individual
with highest fitness value is shown in Figure 4. This optimal
subset of features is showing the set of attributes which are at
high risk while diagnosing heart disease.
Figure 4. GA Chromosome showing High Risk Features for Heart
Diseases Classification
The reduced feature set obtained through GA includes these
attributes: 3 - Chest pain type, 7 - resting electrographic results,
8 - thalach (maximum heart rate achieved) , 9 - exercise
induced angina, 11 - slope (the slope of the peak exercise ST
segment), 12 - ca (number of major vessels colored by
floursopy) and 13 - Thal.
Scenario 4: The PSO algorithm is also employed for the
selection of best feature set. The quality (fitness) of a feature
set is evaluated through SVM classifier. In this way
classification precision is attained by the feature subset
(particle) as its final best point. The highest classification
accuracy of 88.24% is obtained with RBF kernel function of
SVM (Sigma value = 15) and SMO hyperplane calculation
method.
The PSO global best particle β (final best point in search
space) with high risk features is shown in Figure 5. Digit '1' in
the figure represents the high risk features: i.e., 3, 7, 8, 9, 11,
12, 13.
Figure 5. The Global Best Particle β of PSO Representing High Risk
Features.
Comparison of heart diseases classification techniques
Overall results of the proposed hybrid approach and other
different hybrid and non-hybrid techniques for heart diseases
classification and associated risk factors identification are
given in Table 2. The classification results achieved by the
proposed approach in four scenarios (SVM-LS-Linear, SVM-
SMO-RBF, GA-SVM-SMO-RBF and PSO-SVM-SMO-RBF)
have outperformed the other techniques for heart diseases
classification [8-10].
Table 2. Comparison of classification results of different techniques
for multiclass heart disease problem.
Classification Algorithms Accuracy (%)
SVM-Linear [18] 86.62
SVM-Polynomial [19] 83.9
Iftikhar/Fatima/Rehman/Almazyad/Saba
3454 Biomed Res- India 2017 Volume 28 Issue 8
GA-SVM [19] 72.55
SFM [20] 83.31
OCSFM [20] 86.73
NaïveBayes [18] 78.93
ANN [18] 86.04
SVM-LS-Linear (Proposed simple approach) 87.56
SVM-SMO-RBF (Proposed simple approach) 87.95
GA-SVM-SMO-RBF (Proposed hybrid approach) 88.10
PSO-SVM-SMO-RBF (Proposed hybrid approach)
Conclusion
In this paper, an evolution based hybrid approach is proposed
which exploits SVM classifier, GA and PSO optimization
techniques for the classification of multiple states of heart
disease. GA and PSO algorithm are used to select less but
discriminative features in order to significantly improve SVM
classification accuracy. The population-based evolutionary
algorithms GA and PSO found the same reduced optimal
feature set and best final point respectively. Therefore, the
search space for identifying the best solution during heart
disease data analysis is reduced that in turn reduced the
computing rescores consumption. Moreover, a patient visiting
a cardiologist for the examination of a heart disease, he may be
examined for the discovered optimal subset of features
(symptoms) in less time with less effort. The optimal feature
set is obtained with high accuracy and shown high risk
associated with the presence of a particular type of heart
disease in a patient.
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*Correspondence to
Tanzila Saba
College of Computer and Information Sciences
Prince Sultan University Riyadh, 11586
Saudi Arabia
A hybrid approach for heart diseases classification and associated risk factors identification
Biomed Res- India 2017 Volume 28 Issue 8 3455
