aceExplainable machine learning for identifying mild cognitive impairment in older adults with chronic diseases: Model development and temporal validation
Objective This study aimed to develop and validate an interpretable machine-learning classification model for identifying MCI at the time of assessment among older adults with chronic conditions. We also evaluated model performance and the stability of TreeSHAP-based feature explanations across survey waves. Methods Data were obtained from the CHARLS. A total of 8222 participants from the 2018 wave were stratified by MCI status and randomly divided into a development set (n = 5754) and an internal validation set…
XGBoost model achieved moderate and relatively stable discrimination for identifying current MCI status among older adults with chronic conditions across 2018 internal and 2020 temporal validation, with highly stable TreeSHAP explanations.
Model had limited sensitivity and calibration, with sensitivity dropping to 0.390 in temporal validation and Brier score worsening to 0.137, limiting clinical utility.
Model showed limited sensitivity and calibration and requires further recalibration and external validation before clinical implementation, and should not replace formal diagnosis or predict future decline.
Evidence
- Peer-reviewedActa Psychologica2026-10-03
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Truvace Impact Record TRV-2026-1283, v1: “Explainable machine learning for identifying mild cognitive impairment in older adults with chronic diseases: Model development and temporal validation.” Truvace, 2026-10-05. /record/TRV-2026-1283 (accessed at citation time). sha256 12ba01e4c1a844ae…
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