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An Experimental Study of Various Machine Learning Approaches in Heart Disease Prediction
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 175 - Number 21 |
| Year of Publication: 2020 |
| Authors: Md. Shafiul Azam, Md. Abu Raihan, Humayan Kabir Rana |
10.5120/ijca2020920741
|
Md. Shafiul Azam, Md. Abu Raihan, Humayan Kabir Rana . An Experimental Study of Various Machine Learning Approaches in Heart Disease Prediction. International Journal of Computer Applications. 175, 21 ( Sep 2020), 16-21. DOI=10.5120/ijca2020920741
Abstract
According to recent survey of WHO (World Health Organization) 17.9 million people die each year because of heart related diseases and it is increasing rapidly. With the increasing population and diseases, it has become challenging to diagnosis and treatment diseases at the right time. But there is a light of hope that recent advancements in technology have accelerated the public health sector by advanced functional biomedical solutions. This paper aims to analyze the various machine learning approaches namely Naïve Bayes (NB), Random Forest (RF) Classification, Decision tree (DT), Support Vector Machine (SVM) and Logistic Regression (LR) by employing a qualified dataset for heart disease prediction. This research finds the correlations between the various attributes that are suitable to predict the chances of a heart disease and compares the impact of Principle Component Analysis (PCA) on the accuracy of the above mentioned algorithms.
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