relation: https://khub.utp.edu.my/scholars/15481/
title: Heart Disease Risk Prediction using Machine Learning with Principal Component Analysis
creator: Reddy, K.V.V.
creator: Elamvazuthi, I.
creator: Aziz, A.A.
creator: Paramasivam, S.
creator: Chua, H.N.
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), Naïve 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. © 2021 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2021
type: Conference or Workshop Item
type: PeerReviewed
identifier:   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.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124135402&doi=10.1109%2fICIAS49414.2021.9642676&partnerID=40&md5=9a485af555a98a87391999219dda3377
relation: 10.1109/ICIAS49414.2021.9642676
identifier: 10.1109/ICIAS49414.2021.9642676