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Updated: Jun 29, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
[Cognitive markers to discriminate between mild cognitive impairment and normal ageing]
Nely Rodríguez Rodríguez1, Onésimo Juncos-Rabadán, David Facal Mayo
1Departamento de Psicología Evolutiva y de la Educación. Universidad de Santiago de Compostela. Santiago de Compostela. A Coruña. España. nellypsico1982@yahoo.es
Introduction:
mild cognitive impairment (MCI) has been characterized as a transitional stage between normal ageing and dementia. The aim of the present study was to examine differences between normal ageing and MCI in the performance of several cognitive tests. These differences might serve as differential markers.
Material And Methods:
we performed a longitudinal study (24 months) with two evaluations at 12-monthly intervals using the CAMCOG-R and a verbal learning test [test de aprendizaje verbal España-Complutense (TAVEC)]. The sample was composed of 25 persons aged more than 50 years old (five men and 20 women), distributed into two groups: the control group and the MCI group. To assign persons to either of the two groups, Petersen's MCI criteria were applied to Mini-Mental State Examination (MMSE) scores.
Results:
repeated measures ANOVA (2 groups x 2 assessment) showed significant differences between the MCI and control group in the CAMCOG-R scores in orientation, language, memory, abstract thinking, executive function and global score and in the TAVEC scores for immediate recall and short- and long-term free and clued recall. No significant differences were found between the first and second assessment or in the interaction group assessment.
Conclusions:
the results of the present study confirm that the CAMCOG-R and the TAVEC effectively discriminate between normal ageing and MCI and can be used complementarily.
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