Machine Learning Analysis of Retrospective Data From 503 Hospitalized Older Patients With Type 2 Diabetes to Identify Factors Associated With Cognitive Impairment

BACKGROUND Diabetes is increasingly prevalent among older adults; mild cognitive impairment (MCI) comorbidity in this group represents a major concern. Existing MCI prediction methods are often inaccurate, but machine learning (ML) offers improved potential. This study aimed to identify factors associated with MCI through ML analysis of retrospective data from hospitalized older patients with type 2 diabetes mellitus (T2DM). MATERIAL AND METHODS This retrospective study analyzed data from 503 inpatients older than 60 years with T2DM. Patients were classified into MCI (n=102) and normal (n=401) groups based on Mini-Mental State Examination scores. To minimize overfitting and maximize data utilization, 5-fold cross-validation was used for model training and evaluation. Least absolute shrinkage and selection operator regression identified 8 core predictors from clinical data. Logistic regression, eXtreme Gradient Boosting (XGBoost), and random forest algorithms were employed to construct predictive models. Receiver operating characteristic (ROC) curves were used to compare model performance. RESULTS Key predictors of early MCI included age, body mass index, glycated hemoglobin, C-reactive protein, waist-to-height ratio, presence of diabetic complications, diabetes duration exceeding 5 years, and low education level. The XGBoost model outperformed other algorithms in ROC analysis: area under the curve, 0.892±0.032; accuracy, 0.851±0.028; sensitivity, 0.843±0.031; specificity, 0.859±0.029; and F1 score, 0.834±0.033. CONCLUSIONS The XGBoost model, incorporating these identified factors, demonstrated optimal predictive performance for MCI in older patients with T2DM. It may aid clinical risk stratification and provide a quantitative foundation for early intervention.