A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method
作者： Xiao Liu , Xiaoli Wang , Qiang Su , Mo Zhang , Yanhong Zhu
DOI: 10.1155/2017/8272091
关键词:
摘要: Heart disease is one of the most common diseases in world. The objective this study to aid diagnosis heart using a hybrid classification system based on ReliefF and Rough Set (RFRS) method. proposed contains two subsystems: RFRS feature selection with an ensemble classifier. first includes three stages: (i) data discretization, (ii) extraction algorithm, (iii) reduction heuristic algorithm that we developed. In second system, classifier C4.5 Statlog (Heart) dataset, obtained from UCI database, was used for experiments. A maximum accuracy 92.59% achieved according jackknife cross-validation scheme. results demonstrate performance superior performances previously reported techniques.
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