Author/Authors :
Liu, Xiao School of Economics and Management - Tongji University - Shanghai, China , Wang, Xiaoli School of Economics and Management - Tongji University - Shanghai, China , Su, Qiang School of Economics and Management - Tongji University - Shanghai, China , Zhang, Mo School of Economics and Management - Shanghai Maritime University - Shanghai, China , Zhu, Yanhong Department of Scientific Research- Shanghai General Hospital - School of Medicine - Shanghai Jiaotong University - Shanghai, China , Wang, Qiugen Shanghai General Hospital - School of Medicine - Shanghai Jiaotong University - Shanghai, China , Wang, Qian Shanghai General Hospital - School of Medicine - Shanghai Jiaotong University - Shanghai, China
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.
