Using machine learning models to predict the risk of mild cognitive impairment for COPD patients: A multi-cohort study

Abstract

Purpose

The evaluation of cognitive function in patients with chronic obstructive pulmonary disease (COPD) is critical. This study aimed to construct a risk prediction model capable of predicting the rsik of mild cognitive impairment (MCI) in COPD patients using machine learning methods.

Methods

This study enrolled 2142 COPD patients from the China Health and Retirement Longitudinal Study (CHARLS), analyzing 16 sociodemographic, health, and psychological indicators. The predictors screened by LASSO regression were integrated with five machine learning models—Logistic Regression, Neural Network, XGBoost, LightGBM, and Random Forest —to identify the optimal model for predicting MCI risk in COPD patients. External validation was conducted using the Health and Retirement Study dataset (HRS). Model performance underwent comprehensive evaluation across three critical dimensions: discrimination capacity (AUC-ROC), calibration accuracy (Brier score and calibration curves), and clinical applicability (decision curve analysis). Additionally, the SHAP method was employed to elucidate the feature contributions to the final model.

Results

The analysis included 2142 COPD patients, of whom 420 were diagnosed with MCI. 11 predictive variables were selected to construct five machine learning algorithms. Among these, the LightGBM model demonstrated superior performance across all evaluation dimensions - including discrimination capacity, calibration accuracy, and clinical utility - achieving AUROC values ranging from 0.722 to 0.782.The eight most significant features for predicting MCI in COPD patients were sex, residence, instrumental activities of daily living (IADL) score, educational level, social activity engagement, marital status, Internet use and depression.

Conclusion

The machine learning model accurately predicts MCI risk in COPD patients, enabling timely clinical interventions and improved care through early identification of high-risk individuals.