Fusion-Based Machine Learning Architecture for Heart Disease Prediction

作     者：Muhammad Waqas Nadeem Hock Guan Goh Muhammad Adnan Khan Muzammil Hussain Muhammad Faheem Mushtaq Vasaki a/p Ponnusamy 

作者机构：Faculty of Information and Communication Technology(FICT)Universiti Tunku Abdul Rahman(UTAR)KamparPerak31900Malaysia Department of Computer ScienceLahore Garrison UniversityLahore54000Pakistan Department of Computer ScienceFaculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Computer ScienceSchool of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan Department of Information TechnologyKhwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第67卷第5期

页      面：2481-2496页

核心收录：

学科分类：1002[医学-临床医学] 100201[医学-内科学(含：心血管病、血液病、呼吸系病、消化系病、内分泌与代谢病、肾病、风湿病、传染病)] 10[医学] 

主　　题：Heart disease machine learning support vector machine fuzzy logic fusion cardiovascular 

摘      要：The contemporary evolution in healthcare technologies plays a considerable and signicant role to improve medical services and save human *** disease or cardiovascular disease is the most fatal and complex disease which it is hardly to be detected through our naked eyes,as numerous people have been suffering from this disease *** attacks occur when the ranges of vital signs such as blood pressure,pulse rate,and body temperature exceed their normal *** efcient diagnosis of heart diseases could play a substantial role in the eld of cardiology,while diagnostic time could be *** has been a key challenge for researchers and medical experts to diagnose heart diseases accurately and ***,machine learning-based techniques are used for the diagnosis with higher accuracy,using datasets compiled from former medical patients’*** recent years,numerous studies have been presented in the literature propose machine learning techniques for diagnosing heart ***,the existing techniques have some limitations in terms of their *** this paper,a novel Support Vector Machine(SVM)based architecture for heart disease prediction,empowered with a fuzzy based decision level fusion,is *** SVMbased architecture has improved the accuracy signicantly as compared to existing solutions,where 96.23%accuracy has been achieved.