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Updated: Mar 19, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Clinical validation of MyCog Mobile: development of a parsimonious and clinically interpretable prediction model for
Stephanie Ruth Young1, Elizabeth M Dworak1, Katherina Hauner1,2
1Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, United States.
Objectives:
To develop and validate a prespecified logistic model for detecting mild cognitive impairment (MCI) using MyCog Mobile, a self-administered smartphone-based screening application, and to evaluate a structured simplification that reduces patient burden while maintaining diagnostic accuracy.
Materials And Methods:
We analyzed data from 277 older adults (100 electronic health record-confirmed MCI; 177 normal cognitive aging). Guided by the Harrell/Regression Modeling Strategies framework, a prespecified 10-predictor model was compared against reduced models using Wald χ 2 partitioning. Internal validation used 2000 bootstrap (BS) resamples to calculate optimism-corrected C-statistics/area under the receiver operating characteristic, calibration, and clinical utility via decision curve analysis. Sensitivity analyses compared the primary model to bootstrapped and cross-validated regularized regression approaches (LASSO, Ridge, Elastic Net) to confirm model stability.
Results:
The final parsimonious model included 6 predictors and achieved an optimism-corrected C-statistic of 0.812 with excellent calibration (slope = 0.92). Detection accuracy was 75% (BS 95% CI, 69%-80%), consistent with penalized regression models in sensitivity analyses (accuracy 72%-73%), with overlapping CIs confirming predictive stability. Decision curve analysis showed the model provides net benefit over both "refer-all" and "refer-none" strategies across all examined thresholds, capturing ∼55% of the net benefit achievable by a theoretically perfect screener.
Conclusion:
The final model prioritized parsimony to reduce patient burden while maintaining clinical accuracy to detect MCI. Stability across traditional regression and regularized regression approaches from the statistical learning literature indicated a robust predictive signal. Findings support MyCog Mobile as an accurate and accessible cognitive screener able to detect the earliest signs of cognitive impairment in primary care.
