Development and Internal Validation of a Machine Learning–Based Classification Model for Identifying Cognitive Frailty in Older Inpatients with Type 2 Diabetes Mellitus

Background: Cognitive frailty (CF) is prevalent in older adults with type 2 diabetes mellitus (T2DM) and significantly increases risks of adverse outcomes. Early detection is essential for potential reversibility, requiring efficient bedside identification tools. Methods: This study included 523 consecutive older adults (aged 65 years or older) diagnosed with T2DM at a tertiary care center in Sichuan, China. Within a health ecological framework, 35 candidate variables were collected. Multiple imputation was used for missing data. LASSO regression was applied to the full candidate set to select key features. Six algorithms (logistic regression, decision tree, random forest, support vector machine, XGBoost, and stacking) were trained to identify CF with a 70/30 split (training/test). Performance was evaluated on the test set using AUC, AP, accuracy, sensitivity, specificity, and calibration; SHAP was used to assess model interpretability. Results: Mean age was 74.3± 6.4 years, and CF prevalence was 41.5%. LASSO identified eight indicators: age, cognitive activities, physical exercise, diabetes complications, nutritional status, depressive symptoms, perceived social support, and insomnia symptoms. On the test set, logistic regression achieved the best performance for identification (AUC 0.947, AP 0.936, accuracy 0.879, sensitivity 0.838, specificity 0.910), with good calibration. SHAP analysis revealed that older age, lower frequency of physical exercise, higher scores for depressive symptoms, lower social support, poorer nutritional status, a greater number of diabetes complications, lack of cognitive activities, and higher scores for insomnia symptoms were the major contributors to a higher likelihood of CF. Conclusion: In this single-center hospital-based sample, a parsimonious model using eight readily obtainable features demonstrated strong internal validity for identifying CF in older inpatients with T2DM. This tool may facilitate early screening and clinical decision-making. Prospective studies are warranted to confirm its clinical utility. Study flowchart: ML identifies frailty indicators in older T2DM adults.A flowchart illustrating a study on older adults aged 65 years and above with type 2 diabetes mellitus, totaling 523 participants. The study focuses on physical frailty and cognitive impairment without dementia, using 35 multidimensional variables. The process involves multiple imputation (MICE), LASSO feature selection and six machine learning algorithms. A logistic regression model is applied, achieving an area under the curve of 0.947 and an average precision of 0.936. Eight key identification indicators are highlighted: age, cognitive activities, physical exercise, diabetes complications, nutritional status, depressive symptoms, perceived social support and insomnia symptoms. SHAP analysis is used for further insights. Keywords: cognitive frailty, type 2 diabetes mellitus, older adults, machine learning, identification, health ecological theory