eprintid: 17249
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/72/49
datestamp: 2023-12-19 03:23:40
lastmod: 2023-12-19 03:23:40
status_changed: 2023-12-19 03:07:44
type: conference_item
metadata_visibility: show
creators_name: Karna, V.V.R.
creators_name: Paramasivam, S.
creators_name: Elamvazuthi, I.
creators_name: Chua, H.N.
creators_name: Aziz, A.A.
creators_name: Satyamurthy, P.
title: Novel Feature Engineering for Heart Disease Risk Prediction Using Optimized Machine Learning
ispublished: pub
keywords: Adaptive boosting; Cardiology; Decision trees; Diseases; Heart; Intelligent systems; Learning systems; Nearest neighbor search; Risk assessment; Support vector machines, Causes of death; Disease risks; Feature engineerings; Heart disease; Machine-learning; Optimisations; Principal-component analysis; Relief; Risk predictions; Rotation forests, Principal component analysis
note: cited By 2; Conference of 2022 International Conference on Future Trends in Smart Communities, ICFTSC 2022 ; Conference Date: 1 December 2022 Through 2 December 2022; Conference Code:186671
abstract: Heart disease is the leading cause of death and killing millions of people every year around the world. Various automated intelligent systems to predict the heart disease risk have been developed by current research works. However, these studies have drawbacks such as the inability to pick important features, lack of hyperparameter optimization, and varied performance from one model to another. In this work, proposed an unconventional feature engineering in which a Principal Component Analysis was performed on heart dataset to extract the transformed features and selected significant ones among them using Relief method. The hyperparameters of Support Vector Machine, K-Nearest Neighbors, J4S Decision Tree, AdaBoost Ml, Bagging, and Rotation Forest classifiers were optimized and performed machine learning classification using 10-fold cross-validation. The proposed work produced highest accuracy of 98.43 and AUC of 0.996 using KNN, while the Rotation Forest reached the accuracy of 98.25 and best AUC of 0.997. © 2022 IEEE.
date: 2022
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149127751&doi=10.1109%2fICFTSC57269.2022.10040063&partnerID=40&md5=1158f9a99f8c06142a7a2220a91270b6
id_number: 10.1109/ICFTSC57269.2022.10040063
full_text_status: none
publication: 2022 International Conference on Future Trends in Smart Communities, ICFTSC 2022
pagerange: 158-163
refereed: TRUE
isbn: 9798350334548
citation:   Karna, V.V.R. and Paramasivam, S. and Elamvazuthi, I. and Chua, H.N. and Aziz, A.A. and Satyamurthy, P.  (2022) Novel Feature Engineering for Heart Disease Risk Prediction Using Optimized Machine Learning.  In: UNSPECIFIED.