Abstract:

Diabetes is a prevalent chronic health condition globally, posing significant challenges to healthcare systems and insurance companies due to its associated risks of hospital readmission. Early prediction of readmissions, especially within 30 days, is crucial for directing attention to high-risk patients and optimizing healthcare resources. This study explores the application of machine learning models, including Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), sand Artificial Neural Networks (ANN), to predict diabetic readmissions. A novel approach is proposed, leveraging a guided optimizer for ANN training to enhance classification accuracy and error convergence.

Description:

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