Bagging model with cost sensitive analysis on diabetes data

Sittidech, Punnee, Nai-arun, Nongyao and Nabney, Ian T. (2015). Bagging model with cost sensitive analysis on diabetes data. Information Technology Journal, 11 (1), pp. 82-90.


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.

Divisions: Engineering & Applied Sciences > Systems analytics research institute (SARI)
Additional Information: This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
Uncontrolled Keywords: diabetes,feature selection,classification,bagging,cost-sensitive analysis
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Related URLs: http://ojs.kmut ... rticle/view/689 (Publisher URL)
Published Date: 2015
Authors: Sittidech, Punnee
Nai-arun, Nongyao
Nabney, Ian T. ( 0000-0003-1513-993X)



Version: Published Version

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