Health Management of Type 2 Diabetes Mellitus and Its Complications: A Machine Learning Algorithm-Based Retrospective Study in Chinese Communities.

BACKGROUND

The global burden of diabetes mellitus (DM) and its complications is a major global public health challenge. This study aimed to improve community capacity for DM management by developing a risk prediction model for complications and providing health management recommendations using machine learning (ML).

METHODS

A retrospective analysis was conducted of 4916 type 2 diabetes (T2DM) patients from Shanghai communities. Model I was developed and compared by using the least absolute shrinkage and selection operator (Lasso) regression, support vector machine (SVM), decision tree (DT) and logistic regression (LR). A Bayesian Network (BN) model to uncover potential causal relationships. Model I was evaluated and adjusted using the receiver operating characteristic (ROC) curve, area under the curve (AUC), calibration curve, and decision curve analysis (DCA). The BN model was assessed using AUC, accuracy, specificity, and sensitivity.

RESULTS

Five consistent predictors were identified: disease course, diastolic blood pressure, HbA1c, urinary creatinine, and urinary microalbumin. Model I achieved AUCs of 0.695 (training) and 0.676 (validation), with decision curve analysis showing risk thresholds of 12-92% and 20-92% respectively. The calibration curves showed good calibration. The tree-augmented BN model achieved the AUC of 0.755, accuracy of 0.733, specificity of 0.802 and sensitivity of 0.519.

CONCLUSION

Effective models for predicting complication risk in T2DM patients were developed. T2DM patients with chronic comorbidities, higher income, and longer disease duration as key targets for community management. We recommend prioritizing UMA as a key monitoring indicator and strengthening comprehensive interventions, including health education, dietary self-management, and family-community support.