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Rule Extraction From Support Vector Machines Using Ensemble Learning Approach: An Application for Diagnosis of Diabetes | IEEE Journals & Magazine | IEEE Xplore

Rule Extraction From Support Vector Machines Using Ensemble Learning Approach: An Application for Diagnosis of Diabetes


Abstract:

Diabetes mellitus is a chronic disease and a worldwide public health challenge. It has been shown that 50–80% proportion of T2DM is undiagnosed. In this paper, support ve...Show More

Abstract:

Diabetes mellitus is a chronic disease and a worldwide public health challenge. It has been shown that 50–80% proportion of T2DM is undiagnosed. In this paper, support vector machines are utilized to screen diabetes, and an ensemble learning module is added, which turns the “black box” of SVM decisions into comprehensible and transparent rules, and it is also useful for solving imbalance problem. Results on China Health and Nutrition Survey data show that the proposed ensemble learning method generates rule sets with weighted average precision 94.2% and weighted average recall 93.9% for all classes. Furthermore, the hybrid system can provide a tool for diagnosis of diabetes, and it supports a second opinion for lay users.
Published in: IEEE Journal of Biomedical and Health Informatics ( Volume: 19, Issue: 2, March 2015)
Page(s): 728 - 734
Date of Publication: 19 May 2014

ISSN Information:

PubMed ID: 24860043

Funding Agency:


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