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Hospital Readmission Among Diabetic Patients Is A Major Concern In Healthcare Systems, Leading To Increased Medical Costs, Resource Utilization, And Potential Deterioration Of Patient Health. Accurate Prediction Of Readmission Risk Can Assist Healthcare Providers In Implementing Timely Interventions And Personalized Treatment Strategies. Traditional Methods Of Assessing Readmission Risk Often Rely On Limited Clinical Indicators And Statistical Approaches, Which May Not Fully Capture The Complex Interactions Among Patient Demographics, Medical History, Laboratory Results, Treatment Patterns, And Comorbidities. To Address These Challenges, This Study Proposes A Machine Learning–based Approach For Predicting The Likelihood Of Hospital Readmission Among Diabetic Patients. Various Supervised Learning Algorithms, Such As Logistic Regression, Random Forest, Support Vector Machine, And Gradient Boosting, Are Applied To Patient Datasets To Identify Key Predictive Features And Optimize Performance. Data Preprocessing, Feature Selection, And Model Evaluation Techniques Are Employed To Ensure Robustness And Accuracy. The Experimental Results Demonstrate That Machine Learning Models Can Effectively Classify High-risk Patients And Outperform Traditional Prediction Methods. This Study Highlights The Potential Of Data-driven Approaches In Improving Healthcare Decision-making, Reducing Readmission Rates, And Supporting Proactive Management Of Diabetic Patients.