Clinical Risk Modeling For Re Hospitalization In Diabetes: Insights From Electronic Health Records

Abstract

Hospital Readmissions Are A Major Concern In Healthcare, Impacting Patient Well- Being And Increasing Medical Costs. This Study Focuses On Predicting Hospital Readmission Rates For Diabetic Patients Using Machine Learning Techniques. By Analyzing Patient Demographics, Medical History, And Hospitalization Details, We Aim To Identify Key Risk Factors Contributing To Readmission. The Study Employs Various Classification Models, Including Logistic Regression, Decision Trees, Random Forests, And XGBoost, To Determine The Most Effective Predictive Approach. Our Findings Indicate That Certain Patient Attributes, Such As Time Spent In The Hospital, Number Of Inpatient Visits, And Medication Changes, Play A Significant Role In Readmission Likelihood.

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