Thirty-day Re-Hospitalization Rate Prediction of Diabetic Patients

Thirty-day Re-Hospitalization Rate Prediction of Diabetic Patients

Diabetes is a serious global health problem, and rehospitalization is usually associated with increased mortality and excessive medical burden. With the increasing cost of diabetes to the health care system, rehospitalization is recommended as a goal to reduce health care costs. This paper aims to use data mining technology to accurately predict the 30-day re-hospitalization of diabetic patients. We use the data set from UCI machine learning repository, preprocessing, use feature reduction method to find out the classification results of rehospitalization, and then use frequent set and Apriori algorithm to find the association rules between diabetes mellitus patients and re-hospitalization related variables. The experimental results show that the recursive feature reduction method is effective in combined with SVM can get a better prediction accuracy.