TY - JOUR A2 - Zhao, Xiaopeng AU - Liu, Xiao AU - Wang, Xiaoli AU - Su, Qiang AU - Zhang, Mo AU - Zhu, Yanhong AU - Wang, Qiugen AU - Wang, Qian PY - 2017 DA - 2017/01/03 TI - A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method SP - 8272091 VL - 2017 AB - 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. SN - 1748-670X UR - https://doi.org/10.1155/2017/8272091 DO - 10.1155/2017/8272091 JF - Computational and Mathematical Methods in Medicine PB - Hindawi KW - ER -