A Machine Learning and Traditional Chinese Medicine Constitution-Based Prediction Model for Mild Cognitive Impairment in Community-Dwelling Older Adults.

OBJECTIVE

To develop and validate a nomogram screening model for mild cognitive impairment (MCI) in community-dwelling older adults by integrating Traditional Chinese Medicine (TCM) constitution classification with machine learning-based feature selection, aiming to provide a practical tool for early identification in primary care.

METHODS

A cross-sectional study was conducted among 1,503 older adults (aged ≥60 years) at a community health service center in Guangzhou, China. Data were prospectively collected during standardized community health examinations between January and December 2025. Participants were randomly divided into training (n = 1,052) and validation (n = 451) sets. Four machine learning algorithms-LASSO regression, random forest, decision tree, and XGBoost-were applied to identify stable predictors. Variables selected by all four methods were entered into multivariable logistic regression, and a nomogram was constructed. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA).

RESULTS

The prevalence of MCI, defined by education-adjusted Chinese Mini-Mental Status (CMMS) cutoffs based on Petersen criteria, was 24.1%. Seven independent correlates were identified: increasing age, female sex, Qi-deficiency constitution, Yin-deficiency constitution, elevated serum creatinine, regular physical exercise, and Balanced constitution. The nomogram achieved AUCs of 0.813 (training) and 0.747 (validation), with satisfactory calibration. Adding TCM constitution to a clinical reference model significantly improved predictive performance (NRI > 0, p < 0.05).

CONCLUSION

The nomogram incorporating TCM constitution types demonstrated good discrimination, calibration, and clinical utility for community-based MCI screening, providing a practical tool for early identification and risk stratification in primary care settings.