Bagging model with cost sensitive analysis on diabetes data

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Abstract

Diabetes patients might suffer from an unhealthy life, long-term treatment and chronic complicated diseases. The decreasing hospitalization rate is a crucial problem for health care centers. This study combines the bagging method with base classifier decision tree and costs-sensitive analysis for diabetes patients' classification purpose. Real patients' data collected from a regional hospital in Thailand were analyzed. The relevance factors were selected and used to construct base classifier decision tree models to classify diabetes and non-diabetes patients. The bagging method was then applied to improve accuracy. Finally, asymmetric classification cost matrices were used to give more alternative models for diabetes data analysis.

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Original languageEnglish
Pages (from-to)82-90
Number of pages9
JournalInformation Technology Journal
Volume11
Issue1
StatePublished - 2015

    Keywords

  • diabetes, feature selection, classification, bagging, cost-sensitive analysis

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