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Updated: Nov 24, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Diagnostic accuracy of early cognitive indicators in mild cognitive impairment
Marina Martorelli1, Larissa Hartle1,2, Gabriel Coutinho3
1Pontifícia Universidade Católica do Rio de Janeiro - Rio de Janeiro, RJ, Brazil.
Abstract:
The aging of the population leads to an increase in the prevalence of dementia and mild cognitive impairment (MCI). Alzheimer's disease (AD) is the most common cause of dementia. Recent studies highlight the early non-amnestic deficits in AD and MCI. The European Union report shows the importance of thoroughly assessing cognitive aspects that have been poorly evaluated, such as processing speed (PS), which could represent early indicators of cognitive decline.
Objective:
To analyze the diagnostic accuracy of PS measures in older adults with MCI, AD, and those who are cognitively-healthy.
Methods:
A cross-sectional study was conducted by performing an extensive neuropsychological assessment in three samples: 26 control participants, 22 individuals with MCI, and 21 individuals with AD. Analysis of variance (ANOVA) was employed to test the relationship between dependent variables and the clinical group. Post hoc tests (Bonferroni test) were used when a significant ANOVA result was found. Finally, the Receiver Operating Characteristic (ROC) curve for PS measures was performed in older adults with MCI and AD compared with cognitively-healthy older adults.
Results:
The results showed that deficits in PS measures can be early indicators of cognitive decline in cases of MCI, even when executive functions (EFs) and functionality are preserved. Conversely, AD versus MCI presented differences in PS, EFs, and functionality.
Conclusions:
The ROC analyses showed that PS measures had discriminative capacities to differentiate individuals with MCI, AD, and cognitively-healthy older adults.
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