title: A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method.
abstract: Heart disease is one of the most common diseases in the world. The objective of this study is to aid the diagnosis of heart disease using a hybrid classification system based on the ReliefF and Rough Set (RFRS) method. The proposed system contains two subsystems: the RFRS feature selection system and a classification system with an ensemble classifier. The first system includes three stages: (i) data discretization, (ii) feature extraction using the ReliefF algorithm, and (iii) feature reduction using the heuristic Rough Set reduction algorithm that we developed. In the second system, an ensemble classifier is proposed based on the C4.5 classifier. The Statlog (Heart) dataset, obtained from the UCI database, was used for experiments. A maximum classification accuracy of 92.59% was achieved according to a jackknife cross-validation scheme. The results demonstrate that the performance of the proposed system is superior to the performances of previously reported classification techniques.
authors.affiliation: School of Economics and Management, Tongji University, Shanghai, China.
authors.forename: Xiao
authors.identifier: 0000-0003-4673-8273
authors.initials: X
authors.lastname: Liu
authors.affiliation: School of Economics and Management, Tongji University, Shanghai, China.
authors.forename: Xiaoli
authors.identifier: 0000-0002-5182-7409
authors.initials: X
authors.lastname: Wang
authors.affiliation: School of Economics and Management, Tongji University, Shanghai, China.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Su
authors.affiliation: School of Economics and Management, Shanghai Maritime University, Shanghai, China.
authors.forename: Mo
authors.initials: M
authors.lastname: Zhang
authors.affiliation: Department of Scientific Research, Shanghai General Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China.
authors.forename: Yanhong
authors.initials: Y
authors.lastname: Zhu
authors.affiliation: Trauma Center, Shanghai General Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China.
authors.forename: Qiugen
authors.initials: Q
authors.lastname: Wang
authors.affiliation: Trauma Center, Shanghai General Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China.
authors.forename: Qian
authors.initials: Q
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002309:Cardiology / Q000295:instrumentation* / Q000379:methods; D016208:Databases, Factual; D006321:Heart / Q000502:physiology; D006331:Heart Diseases / Q000000981:diagnostic imaging* / Q000503:physiopathology*; D006801:Humans; D000069550:Machine Learning; D012372:ROC Curve; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software