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Heart Disease Risk Prediction using Machine Learning with Principal Component Analysis

Cardiovascular diseases (CVDs) are killing about 17.9 million people every year. Early prediction can help people to change their lifestyles and to endure proper medical treatment if necessary. The data available in the healthcare sector is very useful to predict whether a patient will have a diseas...

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Main Authors:	Reddy, K.V.V., Elamvazuthi, I., Aziz, A.A., Paramasivam, S., Chua, H.N.
Format:	Conference or Workshop Item
Published:	Institute of Electrical and Electronics Engineers Inc. 2021
Online Access:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124135402&doi=10.1109%2fICIAS49414.2021.9642676&partnerID=40&md5=9a485af555a98a87391999219dda3377 http://eprints.utp.edu.my/29213/
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spelling	my.utp.eprints.292132022-03-25T01:11:54Z Heart Disease Risk Prediction using Machine Learning with Principal Component Analysis Reddy, K.V.V. Elamvazuthi, I. Aziz, A.A. Paramasivam, S. Chua, H.N. Cardiovascular diseases (CVDs) are killing about 17.9 million people every year. Early prediction can help people to change their lifestyles and to endure proper medical treatment if necessary. The data available in the healthcare sector is very useful to predict whether a patient will have a disease or not in the future. In this research, several machine learning algorithms such as Decision Tree (DT), Discriminant Analysis (DA), Logistic Regression (LR), NaÃ¯ve Bayes (NB), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Ensemble were trained on Cleveland heart disease dataset. The performance of the algorithms was evaluated using 10-fold cross-validation without and with Principal Component Analysis (PCA). LR provided the highest accuracy of 85.8 with PCA by keeping 9 components and Ensemble classifiers and attained an accuracy of 83.8 using a Bagged tree with PCA by keeping 10 components. Â© 2021 IEEE. Institute of Electrical and Electronics Engineers Inc. 2021 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124135402&doi=10.1109%2fICIAS49414.2021.9642676&partnerID=40&md5=9a485af555a98a87391999219dda3377 Reddy, K.V.V. and Elamvazuthi, I. and Aziz, A.A. and Paramasivam, S. and Chua, H.N. (2021) Heart Disease Risk Prediction using Machine Learning with Principal Component Analysis. In: UNSPECIFIED. http://eprints.utp.edu.my/29213/
institution	Universiti Teknologi Petronas
building	UTP Resource Centre
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Petronas
content_source	UTP Institutional Repository
url_provider	http://eprints.utp.edu.my/
description	Cardiovascular diseases (CVDs) are killing about 17.9 million people every year. Early prediction can help people to change their lifestyles and to endure proper medical treatment if necessary. The data available in the healthcare sector is very useful to predict whether a patient will have a disease or not in the future. In this research, several machine learning algorithms such as Decision Tree (DT), Discriminant Analysis (DA), Logistic Regression (LR), NaÃ¯ve Bayes (NB), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Ensemble were trained on Cleveland heart disease dataset. The performance of the algorithms was evaluated using 10-fold cross-validation without and with Principal Component Analysis (PCA). LR provided the highest accuracy of 85.8 with PCA by keeping 9 components and Ensemble classifiers and attained an accuracy of 83.8 using a Bagged tree with PCA by keeping 10 components. Â© 2021 IEEE.
format	Conference or Workshop Item
author	Reddy, K.V.V. Elamvazuthi, I. Aziz, A.A. Paramasivam, S. Chua, H.N.
spellingShingle	Reddy, K.V.V. Elamvazuthi, I. Aziz, A.A. Paramasivam, S. Chua, H.N. Heart Disease Risk Prediction using Machine Learning with Principal Component Analysis
author_facet	Reddy, K.V.V. Elamvazuthi, I. Aziz, A.A. Paramasivam, S. Chua, H.N.
author_sort	Reddy, K.V.V.
title	Heart Disease Risk Prediction using Machine Learning with Principal Component Analysis
title_short	Heart Disease Risk Prediction using Machine Learning with Principal Component Analysis
title_full	Heart Disease Risk Prediction using Machine Learning with Principal Component Analysis
title_fullStr	Heart Disease Risk Prediction using Machine Learning with Principal Component Analysis
title_full_unstemmed	Heart Disease Risk Prediction using Machine Learning with Principal Component Analysis
title_sort	heart disease risk prediction using machine learning with principal component analysis
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2021
url	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124135402&doi=10.1109%2fICIAS49414.2021.9642676&partnerID=40&md5=9a485af555a98a87391999219dda3377 http://eprints.utp.edu.my/29213/
_version_	1738656934033096704
score	13.211869

Heart Disease Risk Prediction using Machine Learning with Principal Component Analysis

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