Risk Minimization by Early Readmission Prediction for Diabetes Patients: A Data-Driven Machine Learning Approach

Diabetes poses a significant global healthcare challenge, affecting millions of individuals and straining healthcare systems financially. High hospital readmission rates for diabetic patients illuminate potential shortcomings in both the quality of care provided and the efficiency of healthcare delivery. Developing a reliable method to predict these readmissions is crucial to not only reduce the economic burden on patients and healthcare systems but also to proactively intervene and improve patient outcomes. This study aims to improve care for diabetic patients by reducing readmission costs, ultimately enhancing healthcare outcomes. We propose a machine-learning approach to predict hospital readmissions for diabetic patients. This approach innovatively integrates feature selection, feature engineering, and a specifically tailored SMOTE technique to address the inherent data imbalance challenge and enhance model performance in predicting diabetic patient readmission. We analyze a dataset of 101,776 admissions with 49 features. Feature selection and engineering were implemented followed by an improved SMOTE method for imbalanced data. Leveraging effective feature selection and engineering techniques, Random Forest achieved the highest classification accuracy (93.55%) compared to other existing methods, demonstrating the significant contribution of these preprocessing steps to improved model performance in predicting diabetic patient readmission. Our findings demonstrate the potential of machine learning to predict diabetic patient readmissions, paving the way for improved healthcare delivery and patient outcomes.