Bagging model with cost sensitive analysis on diabetes data

Research output: Contribution to journalArticle

View graph of relations Save citation


Research units


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.


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


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

Employable Graduates; Exploitable Research

Copy the text from this field...