Prediction on diabetes patient's hospital readmission rates

Abstract

Hospital Readmission is considered as an effective measurement of service and care provided within the hospital. Emergency readmission to hospital is frequently used as a measure of the quality of a hospital because a high proportion of readmissions should be preventable if the preceding care is adequate. The objective of this study to develop a model to predict 30-day hospital readmission. We have data of 1-lac diabetes patients with 50 features. We used machine learning algorithms: Logistic Regression, Decision Tree, Random Forest, Adaboost and XGBoost for prediction. We achieved the highest accuracy 94% using Random forest among all other algorithms. The results from this study are encouraging and can help healthcare providers to improve their services.