Journal of Alzheimer's Disease Reports · Published 2026-07-01 · DOI 10.1177/25424823261470033
Peiyue Su, Jiale Li, Yang Hu, Yue Xu, Qianqian Ji, Qi Liu, Xiaoping Huang, Ruitong Liao, Liuqing Li, Yueting Liao, Yunzhang Wang, Yifan Lin, Yiqiang Zhan
Background Timely detection of cognitive concerns can facilitate further assessment, referral, and management; however, cognitive impairment often remains under-detected in primary care settings, representing a missed opportunity for early detection for Alzheimer's disease and related dementias. Objective We aimed to develop and internally validate a machine learning-based cross-sectional concurrent classification model that utilizes routine health check data only, supporting concurrent identification of individuals with Ascertain Dementia 8 (AD8)-defined suspected cognitive impairment who warrant further clinical cognitive evaluation. Methods This cross-sectional study included 7440 adults (aged ≥65 years) from the 2021 Elderly Health Check Program in Shenzhen, China. Suspected cognitive impairment was defined as an AD8 score ≥2. Following data splitting, feature selection was processed via the Boruta algorithm. Five algorithms—logistic regression, random forest, XGBoost, SVM, and EasyEnsemble—were evaluated. Performance was assessed using ROC, PR, accuracy, sensitivity, specificity, PPV, NPV, NNS, DCA, calibration plots, and Brier score. Results The EasyEnsemble model outperformed other algorithms on the independent testing set, with an ROC-AUC of 0.696 (Bootstrap-corrected mean: 0.677; 95% CI: 0.643–0.713). At the clinically optimized threshold of 0.034, the model achieved a sensitivity of 60.6%, a specificity of 73.4% and a NNS of 51.9. DCA indicated a positive net benefit across a threshold range approximately from 0.03 to 0.20, suggesting potential clinical utility for triage in this setting. Conclusions This cross-sectional model provides a pragmatic, low-cost tool for concurrent case-finding of individuals with AD8-defined suspected cognitive impairment in primary care. However, further external validation in diverse geographic regions is mandatory before clinical implementation.
Abstract from DOAJ. Public domain (CC0 1.0).
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Su, P., Li, J., Hu, Y., et al. (2026). Machine learning-based screening for Ascertain Dementia 8-defined suspected cognitive impairment leveraging routine health check data in South China. Journal of Alzheimer's Disease Reports. https://doi.org/10.1177/25424823261470033