Context. Re-admission is the return hospitalization within 30 days from the date of original admission or discharge from hospital. Thecosts of the unplanned re-admissions were estimated to $25 billion per year alone in the U.S. Re-admission rate also has a huge impact onquality of care provided to the patients, cost of health care, and utilization of hospital resources and the image of the care provider. Studies indicate huge potential of savings that can be achieved with incremental performance improvements in detecting cases of preventable re-admissions.

Objectives. In this study we find the different features that helpin predicting readmissions, compare different machine learning techniques and build a model to predict readmissions using one technique.We also propose a framework for implementation of this model in the real world situations.

Methods. To reach the objective, the data of the patients over a period of time were studied to determine the factors that help in identifying re-admissions. Experiments are performed for identifying the features that are more relevant to predict re-admissions and for investigating the most suitable machine learning techniques for this purpose.This model was tested to predict re-admission cases for Acute Myocardial Infarction and Pneumonia.

Results. The features that help in predicting re-admissions are determined,and a model was developed using these features and the selected machine learning algorithm. The model showed good results in predicting re-admissions. The model predicted risk of Acute Myocardial Infarction(c-statistic=0.811), and Pneumonia(c=0.76).

Conclusions. We conclude that our model showed good results in predicting re-admissions. The developed model is discriminative for specific diseases like Acute Myocardial Infarction and Pneumonia. Itis also generalized as it incorporates the features that can be easily available from all of the patient population over the globe.