A Machine Learning Technique for Reducing Hospital Readmissions for Diabetic Diseases

Abstract

The number of readmissions in diabetic diseases keeps increasing from time to time in patients from various hospitals. This brings a dreadful name to the hospital and is also considered as an act of irresponsibility of the doctors. So in order to reduce the readmissions of diabetic patients, we propose an approach which uses a machine learning technique to compare the hospital records of various patients. We have used various diabetic dataset features for our technique to predict the readmission probability rates of patients. We compared our proposed technique with existing Machine Learning algorithms like Random Forest, K-means clustering, Support Vector Machine(SVM) and found the best possible prediction with proposed approach using receiver operating characteristic( ROC) curve.