Early Hospital Readmission Prediction in Diabetic Patients
Using a decade's worth of clinical data (1999–2008) from 130 hospitals, we explored factors influencing early readmission of diabetic patients. I applied multiple machine learning models, from decision trees to logistic regression and optimized random forests, to build a reliable predictor.
The models were evaluated based on precision, recall, F1-score, and AUC. Optimized Random Forests emerged as the top performer, helping healthcare providers take proactive measures and reduce costs.
System Workflow
