Exploring predictive factors of physiological, biochemical indicators, and lifestyle for macrovascular complications in type 2 diabetes: a synthesis of machine learning models.

Background: Traditional risk models for macrovascular complications in type 2 diabetes (T2DM) rely on physiological and biochemical indicators, which may lack long-term follow-up data and thus potentially overlook key variables. Methods: A retrospective cohort study was conducted on 4,186 T2DM patients from the Diabetes Health Management Platform in Hotan, Xinjiang, covering the period from 2015 to 2023. Eight machine learning (ML) algorithms were used, with an 8:2 random split into training (n=3,348) and validation (n=838) sets. Performance was evaluated using the area under the receiver operating characteristic curve (AUC), and feature contributions were analyzed using SHAP values. The clinical applicability was verified through decision curve analysis. Results: The T2DM with macrovascular complications group had significantly higher waist circumference, oropharyngeal abnormalities, and absent lung crackles ( Conclusions: Among patients with vascular complications, the disconnect between health behavior risks and subjective health perception is more pronounced. Elevated body temperature, high blood pressure, triglycerides, and fasting glucose indicate inflammation, increasing cardiovascular risk; moderate regular exercise provides protection.