  Hospital readmissions are expensive and reflect the inadequacies in
healthcare system. In the United States alone, treatment of readmitted diabetic
patients exceeds 250 million dollars per year. Early identification of patients
facing a high risk of readmission can enable healthcare providers to to conduct
additional investigations and possibly prevent future readmissions. This not
only improves the quality of care but also reduces the medical expenses on
readmission. Machine learning methods have been leveraged on public health data
to build a system for identifying diabetic patients facing a high risk of
future readmission. Number of inpatient visits, discharge disposition and
admission type were identified as strong predictors of readmission. Further, it
was found that the number of laboratory tests and discharge disposition
together predict whether the patient will be readmitted shortly after being
discharged from the hospital (i.e. <30 days) or after a longer period of time
(i.e. >30 days). These insights can help healthcare providers to improve
inpatient diabetic care. Finally, the cost analysis suggests that \$252.76
million can be saved across 98,053 diabetic patient encounters by incorporating
the proposed cost sensitive analysis model.
