A COMPARATIVE STUDY ON HEART DISEASE PREDICTION USING DIFFERENT MACHINE LEARNING CLASSIFICATION ALGORITHMS
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Date
2020-01-30
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Abstract
Heart disease is the leading cause of death over the past 10 years all over the world.
The risk factors of heart disease may hold numerous measures and considering all the
measures increases the time of medical practitioners for the decision-making process.
The main aim of this work is to pave the way to maintain the huge datasets with the
appropriate measures and choose the right approach which would provide a faster and
more accurate prediction of heart disease depending on the accuracy rate of the
measures. In this thesis, we examine and compare the performance metrics of different
machine learning classification algorithms to predict heart disease. This comparison
shows the different accuracy, precision, and recall rates of different techniques and the
reasons behind their variations. We used collected data that we collect from the
different hospitals in Bangladesh. We preprocess our collected data and then divide it
into two sections named training and testing datasets. The Logistic Regression, Naive
Bayes Classifier, K-Nearest Neighbor, Support Vector Machine, Decision Tree,
Random Forest, and Multi-Layer Perceptron Neural Networks techniques have been
investigated in this research. By the end of the implementation part, we have found
Random Forest is giving the maximum score in all sectors like accuracy, precision,
recall, and f1 score in our dataset, and Multi-Layer Perceptron is performing very
poorly. Random Forest gives a maximum of 90 percent accuracy and KNN gives the
second-best 85 percent accuracy. Other algorithms like KNN, SVM, and Decision Tree
also show overall good performances. The reasons for variations of these different
techniques by analyzing their characteristics and behavior with respect to the dataset
have been understood by the study conducted for this thesis. The research outcome is
the implementation of machine learning on healthcare data and finding a better
technique for prediction.