relation: https://khub.utp.edu.my/scholars/17614/
title: Big data analytics and classification of cardiovascular disease using machine learning
creator: Narejo, S.
creator: Shaikh, A.
creator: Memon, M.M.
creator: Mahar, K.
creator: Aleem, Z.
creator: Zardari, B.
description: Hundreds of people dying from heart disease almost every day that is how terrific a delayed diagnosis can be. Living in an advanced era full of intelligent systems, the increasing number of deaths can be reduced. This research paper focuses on the development of a cardiovascular disease prediction system particularly a heart disease, by developing machine learning classifiers, for instance, Support Vector Machine (SVM), Decision Tree, and XGBoost Classifiers. We also scaled the features to standardize unconstrained features in data, available in a fixed range for better optimization of models. For efficiency, the classification of features was also done in two categories, Independent features, and dependent features. Furthermore, the performance measures helped with best practices for model assessment classifier performance. Eventually, after tuning hyper-parameters, the results exhibit high accuracy for XGBoost among other trained classifiers. After a comparative analysis, the best-suited algorithm can be utilized for heart disease detection, in the medical field, and regarding the economy, as costly treatments are taken into consideration. This indicates that a non-expert can also attempt for diagnosis without fretting over expensive treatments. © 2022 - IOS Press. All rights reserved.
publisher: IOS Press BV
date: 2022
type: Article
type: PeerReviewed
identifier:   Narejo, S. and Shaikh, A. and Memon, M.M. and Mahar, K. and Aleem, Z. and Zardari, B.  (2022) Big data analytics and classification of cardiovascular disease using machine learning.  Journal of Intelligent and Fuzzy Systems, 43 (2).  pp. 2025-2033.  ISSN 10641246     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132357266&doi=10.3233%2fJIFS-219302&partnerID=40&md5=85102e38bdd75618706bb3de850d6859
relation: 10.3233/JIFS-219302
identifier: 10.3233/JIFS-219302