  Diabetes mellitus is a chronic metabolic disorder that has emerged as one of
the major health problems worldwide due to its high prevalence and serious
complications, which are pricey to manage. Effective management requires good
glycemic control and regular follow-up in the clinic; however, non-adherence to
scheduled follow-ups is very common. This study uses the Diabetes 130-US
Hospitals dataset for analysis and prediction of readmission patients by
various traditional machine learning models, such as XGBoost, LightGBM,
CatBoost, Decision Tree, and Random Forest, and also uses an in-house LSTM
neural network for comparison. The quality of the data was assured by
preprocessing it, and the performance evaluation for all these models was based
on accuracy, precision, recall, and F1-score. LightGBM turned out to be the
best traditional model, while XGBoost was the runner-up. The LSTM model
suffered from overfitting despite high training accuracy. A major strength of
LSTM is capturing temporal dependencies among the patient data. Further, SHAP
values were used, which improved model interpretability, whereby key factors
among them number of lab procedures and discharge disposition were identified
as critical in the prediction of readmissions. This study demonstrates that
model selection, validation, and interpretability are key steps in predictive
healthcare modeling. This will help health providers design interventions for
improved follow-up adherence and better management of diabetes.
