Heart Disease Risk Prediction using Machine Learning with Principal Component Analysis

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.

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Abstract

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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Impact Factor: cited By 0
Uncontrolled Keywords: Cardiology; Data handling; Decision trees; Discriminant analysis; Diseases; Forecasting; Learning algorithms; Logistic regression; Nearest neighbor search; Risk analysis; Risk assessment; Support vector machines, Cardiovascular disease; Data preprocessing; Disease risks; Early prediction; Heart disease; Logistics regressions; Machine-learning; Medical treatment; Principal-component analysis; Risk predictions, Principal component analysis
Depositing User: Ms Sharifah Fahimah Saiyed Yeop
Date Deposited: 25 Mar 2022 01:11
Last Modified: 25 Mar 2022 01:11
URI: http://scholars.utp.edu.my/id/eprint/29213

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