TY  - CONF
A1  - Reddy, K.V.V.
A1  - Elamvazuthi, I.
A1  - Aziz, A.A.
A1  - Paramasivam, S.
A1  - Chua, H.N.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124135402&doi=10.1109%2fICIAS49414.2021.9642676&partnerID=40&md5=9a485af555a98a87391999219dda3377
Y1  - 2021///
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 9781728176666
N1  - cited By 9; Conference of 8th International Conference on Intelligent and Advanced Systems, ICIAS 2021 ; Conference Date: 13 July 2021 Through 15 July 2021; Conference Code:175661
N2  - 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.
TI  - Heart Disease Risk Prediction using Machine Learning with Principal Component Analysis
ID  - scholars15481
KW  - Cardiology; Data handling; Decision trees; Discriminant analysis; Diseases; Forecasting; Learning algorithms; Logistic regression; Nearest neighbor search; Risk analysis; Risk assessment; Support vector machines
KW  -  Cardiovascular disease; Data preprocessing; Disease risks; Early prediction; Heart disease; Logistics regressions; Machine-learning; Medical treatment; Principal-component analysis; Risk predictions
KW  -  Principal component analysis
AV  - none
ER  -