Heart Disease Diagnosis Using Machine Learning

Abstract

The scope of Machine Learning algorithms are increasing in predicting various diseases. The nature of machine learning algorithms to think like a human beings are making this concept important and versatile. Here the challenge of increasing the accuracy of Heart disease prediction is taken upon. In the proposed work, decision support system is made by two supervised machine learning models namely Random Forest and Logistic Regression. The method of predicting heart diseases using Random Forest with well-set attributes predicts the stage of heart failure of the patient. Logistic Regression Analysis is used in the model to infer how confident can the predicted value be actual value when given as test data of a patient details in predicting the stages of heart failure. By the proposed algorithm for heart disease prediction, many lives could be saved in the near future.

Full text

Heart Disease Diagnosis Using Machine Learning
Anusha G C, Apoorva M S, Deepthi N,
Dhanushree V
Student, Department of Computer Science & Engineering,
GSSSIETW, Mysuru
Rummana Firdaus
Assistant Professor,
Department of Computer Science & Engineering,
GSSSIETW, Mysuru
Abstract---- The scope of Machine Learning algorithms are
increasing in predicting various diseases. The nature of
machine learning algorithms to think like a human beings are
making this concept important and versatile. Here the
challenge of increasing the accuracy of Heart disease
prediction is taken upon. In the proposed work, decision
support system is made by two supervised machine learning
models namely Random Forest and Logistic Regression. The
method of predicting heart diseases using Random Forest with
well-set attributes predicts the stage of heart failure of the
patient. Logistic Regression Analysis is used in the model to
infer how confident can the predicted value be actual value
when given as test data of a patient details in predicting the
stages of heart failure. By the proposed algorithm for heart
disease prediction, many lives could be saved in the near
future.
Keywords - Heart Disease, Machine learning, Random Forest,
Logistic Regression.
I.
INTRODUCTION
In the near future, artificial intelligence (AI) techniques,
such as machine learning, deep learning, and cognitive
computing, may play a critical role in the evolution of
cardiovascular (CV) medicine to facilitate precision CV
medicine. CV clinical care currently faces practical
challenges in predicting the stages of heart failure.
Physicians have long needed to identify, quantify, and
interpret relationships among variables to improve patient
care. Hence identifying or predicting the disease at the
earliest is very important to avoid any unwanted casualties.
Machine learning techniques comprise a variety of methods
that allow computers to algorithmically learn efficient
representations of data. Machine learning techniques can be
broadly classified into either unsupervised or supervised
learning. These have different goals. Unsupervised learning
focuses on discovering underlying structure or relationships
among variables in a dataset, whereas supervised learning
often involves classification of an observation into 1 or
more categories or outcomes. Supervised learning thus
requires a dataset with predictor variables and labeled
outcomes.
The proposed system is used to predict the possible risk of
heart disease in patient by analyzing the stages of heart
failure with the help of Random Forest algorithm. Random
forest is a supervised learning algorithm that operates by
constructing multiple decision trees during training phase.
The decision of the majority of the trees is chosen by the
random forest as final decision Random forest develops
lots of decision tree based on random selection of data and
random selection of variables with that it provides class of
dependent variable based on many trees.
II.
RELATED WORKS.
Over the years, a range of works have been done related to
heart disease prediction system using different data
mining algorithms by different authors. They tried to attain
efficient methods and accuracy in finding out diseases
related to heart by their work including datasets and
different algorithms along with the experimental results and
future work that can be done on the system to achieve more
efficient results.
Shadab Adam Pattekari et al. [1] explains that the
main objective of the research is to develop an Intelligent
System using data mining modeling technique, namely,
Naive Bayes. It is implemented as web based application in
this user answers the predefined questions. It retrieves
hidden data from stored database and compares the user
values with trained data set. It can answer complex queries
for diagnosing heart disease and thus assist healthcare
practitioners to make intelligent clinical decisions which
traditional decision support systems cannot. By providing
effective treatments, it also helps to reduce treatment costs.
Ganesha B et al. [2] describes that it is important to
monitor heart rate during cycling. By monitoring heart rate
during cycling, Cyclists can control the cycling session
such as cycling cadence to determine the intensity of
exercise. By controlling the intensity of cycling, cyclists
can avoid risks
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of over training and heart attack. Exercise intensity can be
measured by heart rate of cyclist. The heart rate can be
measured by wearable sensor. But there are data that are not
recorded by the sensor at a regular time for example, one
second, two second, etc. So we need a prediction model of
heart rate to complete the missing data. The purpose of this
study is to create a predictive model for heart rate based on
cycling cadence using Feed forward Neural Network. The
inputs are heart rate and cadence on the second. The output
is the predictive value if heart rate on the next second. Feed
forward Neural Network is used as a mathematical model
of the relationship between heart rate and cycling Cadence.
Kuspraspta Mutijarsa et al. [3] reports that the rapid
growth is seen in health care services over the past few
years. Heart disease causes millions of death worldwide.
Many wireless communication technologies have been
developed for heart disease prediction. Data mining
algorithms are very useful in the detection and diagnosis of
heart disease. In this paper, survey is carried out on several
single and hybrid data mining algorithm in order to identify
the algorithm that best suits the heart disease prediction
with high level of accuracy.
AK Srivas et al. [4] relates that the identification of
diseases are very challenging task in field of medical
science. Heart disease is very critical issues facing by the
people. In our proposed work we have used data mining
based classification techniques for analysis and
classification of different level of heart disease namely
Cleveland, Switzerland, Hungarian and Long Beach. They
have used WEKA (Waikato Environment for Knowledge
Analysis) and Rapid miner data mining tools for analysis of
heart disease data set and compared the performance of
different classification techniques with four heart disease
data set using WEKA and Rapid Miner data mining tool.
The proposed SVM gives better accuracy as 66.67% with
Hungarian data set in case of WEKA data mining tool while
Decision Stump gives better accuracy as 63.94% with same
Hungarian data set in case of Rapid miner data mining tool.
The Hungarian data set gives better performance with our
proposed data mining tools and classification techniques
which can help the people to predict effective factors about
Coronary Heart Disease.
Thomas Ernest Perry et al. [5] details that the
electronic health records possess critical predictive
information for machine-learning- based diagnostic aids.
However, many traditional machine learning methods fail
to simultaneously integrate textual data into prediction
process because of its high dimensionality. In this paper,
they present supervised method using Laplacian
Eigenmaps to enable existing dimensional
representations of textual data and accurate predictors
based on these low- dimensional representations at the
same time. This preserves the local similarities among
textual data in high- dimensional space. The proposed
implementation performs alternating optimization using
gradient descent. For the evaluation, they applied
Laplacian Eigenmaps method to over 2000 patient
records from a large single- center pediatric cardiology
practice to predict if patients were diagnosed with
cardiac disease.
Yeshvendra K. Singh et al. [6] States that the
scope of Machine Learning algorithms are increasing in
predicting various diseases. The nature of machine
learning algorithm to think like a human being is
making this concept so important and versatile. Here
the challenge of increasing the accuracy of Heart
disease prediction is taken upon. The non-linear
tendency of the Cleveland heart disease dataset was
exploited for applying Random Forest to get an
accuracy of 85.81%. The method of predicting heart
diseases using Random Forest with well-set attributes
fetches us more accuracy. Random Forest was built by
training 303 instances of data and authentication of
accuracy was done using 10- fold cross validation. By
the proposed algorithm for heart disease prediction,
many lives could be saved in the future.
Prof. Priya R. Patil et al. [7] conveys that the
accurate diagnosis of a heart disease, is one of the most
important biomedical problems whose administration is
imperative. In their proposed work, decision support
system was made by three data mining techniques
namely Classical Random Forest, Modified Random
Forest and Weighted Random Forest. The classical
random forests constructs a collection of trees. In
Modified Random Forest, the tree is constructed
dynamically with online fitting procedure. A random
forest is a substantial modification of bagging. Forest
construction is based on three step process. 1. Forest
construction,2. The polynomial fitting procedure and 3.
The termination criterion In Weighted Random Forest,
The Attribute Weighting Method is used for improving
Accuracy of Modified Random Forest. There are Two
Techniques are used in Attribute Weighting: 1.
Averaged One- Dependence Estimators (AODE) 2.
Decision Tree-based Attribute Weighted Averaged
One-dependence Estimator (DTWAODE).
Evanthia E Tripoliti et al. [8] states that, the accurate
diagnosis of diseases with high prevalence rate, such as
Alzheimer, Parkinson, diabetes, breast cancer, and heart
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diseases, is one of the most important biomedical problems
whose administration is imperative. In this paper, they
present a new method for the automated diagnosis of
diseases based on the improvement of random forests
classification algorithm. More specifically, the dynamic
determination of the optimum number of base classifiers
composing the random forests was addressed. The proposed
method was different from most of the methods reported in
the literature, which followed an overproduce-and-choose
strategy, where the members of the ensemble were selected
from a pool of classifiers, which was known a priori. In
their case, the number of classifiers were determined during
the growing procedure of the forest. Additionally, the
proposed method produced an ensemble not only accurate,
but also diverse, ensuring the two important properties that
should characterize an ensemble classifier. The method was
based on an online fitting procedure and it was evaluated
using eight biomedical datasets and five versions of the
random forests algorithm (40 cases). The method correctly
decided the number of trees in 90% of the test cases.
Liaw et al, [9] have noted from their experiments
that the number of trees necessary for good performance
grows with the number of predictors. The best way to
determine how many trees are necessary is to compare
predictions made by a forest to predictions made by a
subset of a forest. When the subsets work as well as the
full forest, it means that there are enough trees. A lot of
trees are necessary to get stable estimates of variable
importance and proximity. However, their experience has
been that even though the variable importance measures
may vary from run to run, the ranking of the importance
is quite stable. For classification problems where the class
frequencies are extremely unbalanced (e.g., 99% class 1
and 1% class 2), it was necessary to change the prediction
rule to other than majority votes. For example, in a two-
class problem with 99% class 1 and 1% class 2, they
suggested that one may predict the 1% of the
observations with largest class 2 probabilities as class 2,
and use the smallest of those probabilities as threshold for
prediction of test data (i.e., use the type=’prob’ argument
in the predict method and threshold the second column of
the output). They have routinely done this to get ROC
curves. By default, the entire forest was contained in the
forest component of the random forest object. It took up
quite a bit of memory for a large data set or large number
of trees. If prediction of test data was not needed, they set
the argument keep. forest=FALSE when running Random
Forest. This way, only the tree was kept in memory at any
time, and thus lots of memory (and potentially execution
time) were saved. Since the algorithm falls into the
“embarrassingly parallel” category, one can run several
random forests on different machines and then aggregate
the votes component to get the final result.
Vrushali Y Kulkarni et al. [10] surveys that
Ensemble methods aim at improving classification
accuracy by aggregating predictions from multiple
classifiers. More diverse the base classifiers and less are
they correlated; the more is accuracy of the ensemble.
Random Forest algorithm uses- (i) Sub-sampling the
examples/cases as in bagging, (ii) Sub-sampling the
features known as feature selection. Both these
strategies were used in Random Forest to introduce
randomization and achieve diversity. Also, there was no
pruning in the base decision trees to ensure diversity
among them. They have stated that the use of Fuzzy
decision trees and Semi supervised learning with
Random Forest is the recent development and there is
future scope for semi supervised learning with Random
Forest due to capability of handling both labeled and
unlabeled data; especially for scenarios where getting
labeled data is a problem. They have presented
Taxonomy of Random Forest algorithm and have
performed analysis of various algorithms and
techniques based on Random Forest algorithm. Their
analysis is presented as Comparison chart to serve as a
guideline for pursuing future research related to
Random forest classifier.
III.
PROBLEM FORMULATION
Heart plays a crucial role in circulatory system. When
heart does not function properly then it will lead to serious
health conditions including death. According to the World
Health Organization, in the year 2016, 17.9 million people
died from heart disease representing 31% of all global
deaths.
The aim of this paper is to present an intelligent predicting
system that predicts the stage of heart failure in patients
which could help in the earlier detection and treatment
effectively. The main objective of this paper is to develop
an Intelligent Heart disease prediction system using
machine learning techniques.
IV.
METHODOLOGY
Medical experts convert the data into Electronic Health
Record (EHR). Data labeling is the collection of data
which consists of features and target value.
Training dataset of heart disease is taken from UCI
repository. The dataset consists of 303 instances taken
from the observations made at the Cleveland Clinic
Foundation (cleveland.data). The target values 1,2,3,4
corresponds to stages of heart failure as defined by
American Heart Association (AHA) and 0 specifies no
heart disease.
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Data
Labelling
Medical Expert
Report
Generation
Heart Disease Prediction
Model
The dataset consists of 76 attributes. According to the
researchers, 13 attributes are considered as best fit for the
prediction system.
Training
dataset
Figure 1: System Architecture
In the above Figure 1, the medical expert is the user of the
system who is responsible for loading the data of patients
into the system in the desired format as clinical dataset
against which the prediction is made. The datasets are taken
through feature selection process of selecting the most
appropriate features into the classification and prediction
algorithm. Feature selection is a process of identifying and
removing redundant, irrelevant features and increasing
accuracy. The motivation for applying the feature selection
has been increased for model building.
The algorithm trains the model against test dataset and a
detailed report of the heart disease analysis is generated.
This report is used as an advice sheet to the doctors or
medical experts to help faster and more accurate diagnosis
of patient’s health and save their lives.
The algorithms proposed in this paper are Random Forest
and Logistic Regression. Random Forest is a supervised
learning algorithm that builds multiple decision trees and
merges them together to get a more accurate and stable
prediction.
Figure 2: Random Forest Algorithm
The Random Forest algorithm operates by constructing
multiple decision tress during training phase. The decision
of the majority of the trees is chosen by the random forest
as final decision. The “forest” it builds, is an ensemble of
Decision Trees, most of the time trained with the
“bagging” method. The general idea of the bagging
method is that a combination of learning models increases
the overall result.
Thus, Random Forest is an ensemble classifier which
combines bagging and random selection of features. Here,
the algorithm predicts the stage of heart failure of the
patient details given as test data through random selection
and bagging method of 14 features that are considered in
the training data set.
Logistic Regression is a machine learning algorithm based
on supervised learning. This is used to infer how
confident can predicted value be actual value when given
as test data of a patient details in predicting the stages of
heart failure.
Thus these above mentioned two algorithms under the
class of supervised learning are by far the most
appropriate and scalable algorithms for our problem
specified.
V.
RESULTS
The results obtained after the implementation of the
model specified are mentioned below.
For every test data that contains patient details that has the
attributes specified in exact reflection of the training data,
we obtain the stage of heart failure in the patient as
predicted by the model that uses random forest and logistic
regression.
Figure 3: Model detects the presence of heart disease
This phase of the model obtains patient details as test data
and with the classify option it predicts whether the patient
has heart disease or not.
At this stage the model gives only the presence of heart
disease in the patient specified.
Clinical
dataset
Test
Dataset
Feature
Selection
Classification
Random Forest
Logistic
Regression
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Figure 4: Stages of heart failure in patient along with the treatment
suggestions
At this stage the model generates the report about the stage
of heart failure in the patient predicted by the two
algorithms along with the Generic characteristics of
patients of the corresponding stage and its treatment details
and suggestions.
Figure 5: The graph of accuracy of the model
The figure 5 is the graph of accuracy obtained by true
positive values of the confusion matrix plotted for every
303 instances in the training dataset in the intervals of 50
instances for the random forest and Logistic regression
algorithms.
This shows that the performance of random forest is
remarkable over the logistic regression.
VI.
DISCUSSION
The problem statement is considered from the real world
human health oriented issue in our intention to provide
enhanced effectiveness towards the treatment through a
technological solution based on the current technology of
machine learning.
This paper approaches with the valid dataset taken from
the UCI repository of Cleveland Clinic Foundation as the
training data to construct the model. The stages of heart
failure and its generic patient characteristics, treatment
details and goals considered are according to the
specification made by the American Heart Association
(AHA).
The choice of supervised learning algorithms were based
on the hardware and software requirements along with the
nature of the training dataset and the solutions that were
intended to be provided. Thus, the paper details the
application of random forest and logistic regression
algorithms for designing this prediction model.
VII.
CONCLUSION
The Intelligent Heart disease Prediction system developed
can lead to proper selection of treatment methods for a
patient diagnosed with stage of heart failure. This system
helps physicians in early diagnosis that makes cost
reductions in prevention and treatment of the heart
condition in patients, and avoids mortality rates.
VIII.
FUTURE ENHANCEMENTS
The model produces the report with stage of heart failure
and the cumulative characteristics of patients with
corresponding stage, its treatment details and goals.
According to Fakhraei et al. [11] data obtained during the
four phases of data analysis during the drug development
process can be constructed as training data for each stage
of heart failure which shall lead to the prediction of
necessary pharmaceutical discoveries for individual stages
that can be predicted along with the stage of heart failure .
Thus an addition to the report would be the pharmaceutical
discoveries made for each stage of heart failure along with
the current details.
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