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Updated: Mar 5, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Predicting dementia using socio-demographic characteristics and the Free and Cued Selective Reminding Test in the
Thibault Mura1,2,3, Marieta Baramova4, Audrey Gabelle4,5,6
1Neuropsychiatry: Epidemiological and Clinical Research, INSERM U1061, 34093, Montpellier, Cedex 5, France. t-mura@chu-montpellier.fr.
Background:
Our study aimed to determine whether the consideration of socio-demographic features improves the prediction of Alzheimer's dementia (AD) at 5 years when using the Free and Cued Selective Reminding Test (FCSRT) in the general older population.
Methods:
Our analyses focused on 2558 subjects from the prospective Three-City Study, a cohort of community-dwelling individuals aged 65 years and over, with FCSRT scores. Four "residual scores" and "risk scores" were built that included the FCSRT scores and socio-demographic variables. The predictive performance of crude, residual and risk scores was analyzed by comparing the areas under the ROC curve (AUC).
Results:
In total, 1750 subjects were seen 5 years after completing the FCSRT. AD was diagnosed in 116 of them. Compared with the crude free-recall score, the predictive performances of the residual score and of the risk score were not significantly improved (AUC: 0.83 vs 0.82 and 0.88 vs 0.89 respectively).
Conclusion:
Using socio-demographic features in addition to the FCSRT does not improve its predictive performance for dementia or AD.
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