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Updated: Jan 17, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Accuracy of the Mini-Mental State Examination and Montreal Cognitive Assessment in Detecting Cognitive Impairment in
Paula Andreatta Maduro1,2,3, Leandro Paim da Cruz Carvalho2,4, Luiz Alcides Ramires Maduro2,5
1Programa de Pós-Graduação em Ciências da Saúde (PPGCS), Universidade de Pernambuco (UPE), Recife 50100-130, PE, Brazil.
Abstract:
Early detection of cognitive decline in older adults is essential for implementing timely interventions. This study aimed to compare the diagnostic accuracy of the Mini-Mental State Examination (MMSE®) and the Montreal Cognitive Assessment (MoCA©) in identifying cognitive impairment among community-dwelling older adults, while considering the effect of educational level. A cross-sectional, analytical study was conducted with 90 individuals aged 60 years or older, classified into cognitively preserved and cognitively impaired groups using the Clinical Dementia Rating (CDR) scale. Cognitive performance was assessed using the MMSE and MoCA, with results analyzed using both standard and education-adjusted cut-off scores. Diagnostic accuracy was evaluated using Receiver Operating Characteristic (ROC) curves. The MoCA demonstrated superior discriminative ability compared to the MMSE, with a significantly larger area under the ROC curve (AUC = 0.943 vs. 0.826; p < 0.001), higher sensitivity (90.2% vs. 78.4%), and higher specificity (87.2% vs. 76.9%). When education-adjusted cut-off scores were applied, the MoCA achieved markedly improved diagnostic accuracy (87.8%) compared to the MMSE (71.1%), with stronger agreement with CDR classifications (κ = 0.746 vs. κ = -0.132). These findings demonstrate that the MoCA is more sensitive in detecting cognitive impairment and should be considered the preferred screening tool in clinical and research settings, particularly when appropriate educational adjustments are applied.
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