Development and validation of a machine learning model for early screening of high-risk mild cognitive impairment from the multi-cohort data.

BACKGROUND

Early screening of mild cognitive impairment (MCI) in older populations is crucial for timely intervention. MCI often precedes dementia, but current diagnostic tools are time-consuming and not widely accessible. Utilizing basic physical examination data may enable earlier, more practical screening.

METHODS

Data from the China Health and Retirement Longitudinal Study (CHARLS) 2015 were used to develop the model. Two external datasets from CHARLS 2011 and Yiwu 2021 cohorts were used for validation. A total of 34 variables were considered, including demographics, health conditions, lifestyle, and physical and blood examination data. The Mini-Mental State Examination (MMSE) was used for MCI diagnosis. Seven key variables (education, grip strength, height, weight, creatinine, mean corpuscular volume, and platelet count) were selected through majority voting. Five machine learning models were evaluated, and a Random Forest (RF) model was chosen based on its superior performance.

RESULTS

The model demonstrated high diagnostic performance with a sensitivity of 0.906, specificity of 0.850, and accuracy of 85.5%. The area under the receiver operating characteristic curve (AUROC) was 0.93, and the area under the precision-recall curve (AUPRC) was 0.93. In the external validation, AUROCs of 0.83 and 0.87 were achieved. The model was enhanced with an explainable method and deployed via a Streamlit-based web application.

CONCLUSIONS

This study successfully developed machine learning-based models for early MCI screening in older populations via basic physical examination data and MCI risk prediction through a web calculator (https://mciscreening.streamlit.app/), both demonstrating favorable performance, generalizability, and effective clinical implementation.