a hybrid classification system for heart disease diagnosis based on the rfrs method

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ID: 186117
2017
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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.
Reference Key
liu2017computationala Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Xiao Liu;Xiaoli Wang;Qiang Su;Mo Zhang;Yanhong Zhu;Qiugen Wang;Qian Wang
Journal advanced functional materials
Year 2017
DOI
10.1155/2017/8272091
URL
http://dx.doi.org/10.1155/2017/8272091
Keywords
medical informatics computer applications to medicine

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