Multivariate logistic analysis.

BackgroundWith the advancement of globalization, the prevalence of cognitive dysfunction in the elderly population has risen significantly. Early intervention may dramatically alleviate the disease burden and reduce economic costs associated with cognitive impairment. This study aims to construct a risk prediction model for cognitive dysfunction based on machine learning (ML) algorithms, providing healthcare professionals and patients with a more accurate and effective tool for risk assessment.MethodsThis study included 1,325 elderly participants who completed cognitive assessments and comprehensive laboratory blood tests. Risk factors for cognitive dysfunction were identified through univariate analysis, multivariate logistic regression, LASSO regression, and the Boruta algorithm. Nine ML methods—Random Forest (RF), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), Logistic Regression, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree, and Elastic Net—were employed to construct the prediction models. The Shapley Additive Explanations (SHAP) algorithm was utilized to interpret the final model.ResultsThe Random Forest model exhibited the highest predictive performance, with an AUC value exceeding those of other models. SHAP analysis identified age, race, education level, diabetes, and depression as the primary predictors of cognitive dysfunction in the elderly. The calibration curve indicated a strong alignment between the model’s predictions and actual outcomes, while the decision curve confirmed the model’s clinical applicability.ConclusionAge, race, education level, diabetes, and depression are significant influencing factors of cognitive dysfunction in the elderly. Among the ML algorithms evaluated, the Random Forest model exhibited the best predictive performance.