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Updated: Aug 7, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Cognitive screening for MCI in low-educated and illiterate older adults: comparative diagnostic accuracy of four
Deniz Cengiz1, Ceyda Kayabaşı2, Murat Pehlivan2
1Division of Geriatric Medicine, Department of Internal Medicine, Hacettepe University, 06230, Ankara, Turkey. deniz.sahin232@gmail.com.
Purpose:
Cognitive assessment in older adults with low educational attainment presents a diagnostic limitation. Conventional instruments may show reduced accuracy in populations with limited literacy. Therefore, we aimed to compare the diagnostic performance of four cognitive screening instruments for detecting mild cognitive impairment (MCI) in older adults with low education.
Methods:
In this prospective study, adults aged ≥65 years with educational attainment at or below the primary school level were recruited from a geriatric outpatient clinic. Participants underwent assessment with the RUDAS, QMCI-TR, S-MMSE, and DemTect. MCI was diagnosed according to Petersen criteria by geriatricians blinded to cognitive screening test results. Receiver operating characteristic analysis and multivariable logistic regression were performed.
Results:
A total of 242 participants (mean age 74.0 ± 5.8 years; 62.0% female) were included, of whom 100 (41.3%) had MCI. RUDAS demonstrated comparatively better diagnostic accuracy (AUC 0.740, 95% CI 0.666-0.798), with a cut-off value of ≤23 yielding 83.0% sensitivity and 73.2% specificity. QMCI-TR and S-MMSE showed lower discriminative performance (AUCs 0.666 and 0.631, respectively), while DemTect showed no significant discriminative value (AUC 0.464). RUDAS remained the strongest independent predictor of MCI in multivariable analysis.
Conclusion:
Cognitive screening performance varied substantially across instruments in older adults with low educational backgrounds. While RUDAS showed the highest, albeit modest, diagnostic accuracy, other tools showed more limited or no diagnostic utility. Given that MCI represents a high-risk state for progression to dementia, these findings highlight the potential for misclassification, underscoring the need for appropriate test selection to improve early detection.
