Citation

Tushar, Alimul Mahfuz and Wazed, Abdul and Shawon, Ehsanuzzaman and Rahman, Muntasir and Hossen, Md. Ismail and Mohd Zebaral Hoque, Jesmeen (2022) A Review of Commonly used Machine Learning Classifiers in Heart Disease Prediction. In: 2022 IEEE 10th Conference on Systems, Process & Control (ICSPC), 17 Dec 2022, Malacca, Malaysia.

Text 19.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

Last couple of years a lot of people are dying because of heart related disease and now this is one of the most concerning and life-threatening disease all over the world. It is also a concerning matter for health industry. About one person dies from heart disease every minute in the modern era. As heart disease prediction is a critical task, there is a need to automate the prediction process to avoid risks associated with it and inform the patient in advance. So, there is need a system or technique to diagnose this disease with maximum accuracy. Machine learning algorithm and technique can be helpful for health care industry because it has the ability to analyze large and complex data set. In this paper, we will exhibit how to utilize various kinds of machine learning models likes Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Naïve Bayes, Decision Trees (DT), Random Forest (RF), Logistic Regression and predicts the chances of heart disease and classifies patient's risk.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Support Vector Machines (SVM) , K-Nearest Neighbor (KNN) , Naive Bayes , Decision Trees (DT) , Random Forest (RF) , Logistic Regression , Machine learning , heart disease
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	07 Mar 2023 01:45
Last Modified:	07 Mar 2023 01:45
URII:	http://shdl.mmu.edu.my/id/eprint/11199

Downloads

Edit (login required)