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.
|Number of pages||9|
|Journal||Information Technology Journal|
|Publication status||Published - 2015|
Bibliographical noteThis 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.
- feature selection
- cost-sensitive analysis