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.

该研究使用了 Diabetes 130-US Hospitals 数据集，利用各种传统机器学习模型（如 XGBoost、LightGBM、CatBoost、决策树和随机森林）进行分析和预测了糖尿病患者的再入院情况，并通过自主开发的 LSTM 神经网络进行对比。研究结果显示，LightGBM 是最佳的传统模型，而 XGBoost 位居第二，LSTM 模型在训练准确性较高的情况下存在过拟合问题。研究还使用了 SHAP 值提高了模型的解释性，并指出了实验结果中影响再入院预测的重要因素，如实验室过程数量和出院安排。该研究表明，在预测性医疗模型中，模型选择、验证和解释性是关键步骤，有助于医疗服务提供者设计干预措施，提高糖尿病患者的随访遵从性和管理效果。