Predicting Hospital Readmission for Diabetes Patients
Keywords:
Diabetes Readmission Prediction, Machine Learning (ML), Deep Learning (DL), ClassificationAbstract
Predicting hospital readmission among diabetes patients is essential for improving patient outcomes, reducing healthcare costs, and optimizing the use of medical resources. However, this task is complex due to the intricate nature of healthcare data, high feature dimensionality, class imbalance issues, and the necessity of integrating both demographic and clinical variables. To address these challenges, a variety of machine learning models were developed and assessed, including traditional classifiers such as Decision Trees, Logistic Regression, and Random Forests, as well as more advanced approaches like XGBoost and Deep Neural Networks. To enhance model performance, we applied preprocessing techniques such as feature transformation, data balancing, and categorical encoding. Experiments were conducted on clinical datasets to predict patient readmission within 30 days, after 30 days, or not at all. Performance metrics included classification accuracy and the AUC-ROC score. Results showed that the Random Forest model achieved the highest performance in binary classification, with an accuracy of 94% and an AUC-ROC of 0.97, while a proposed Multi-Stage Classifier excelled in the multi-class task with 80% accuracy and an AUC-ROC of 0.89. Overall, the study highlights the potential of machine learning, particularly when coupled with effective preprocessing, to accurately predict hospital readmissions in diabetes care, thereby aiding clinical decisions and improving healthcare efficiency.
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