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Updated: Feb 17, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
A new Brief computerized cognitive screening battery (CompCogs) for early diagnosis of Alzheimer's disease
Helenice Charchat Fichman1, Ricardo Nitrini2, Paulo Caramelli3
1Department of Psychology, Catholic University, Rio de Janeiro, Brazil.
Abstract:
Screening tests for early diagnosis of dementia are of great clinical relevance. The ideal test set must be brief and reliable, and should probe cognitive components impaired in Alzheimer's disease (AD).
Objectives:
To develop a new Computerized Cognitive Screening test (CompCogs), and to investigate its validity for the early diagnosis of AD, and evaluate its heuristic value in understanding the processing of information in AD.
Methods:
The computerized neuropsychological performance battery, originally including six tests, was applied in forty seven patients with probable mild AD and 97 controls matched for age and education. This computerized neuropsychological test battery, developed with MEL Professional, allows control of timing and order of stimuli presentation, as well as recording of response type and latency. A brief-screening version, CompCogs, was selected using the most discriminative neuropsychological test variables derived from logistic regression analysis. Full battery administration lasted about 40 minutes, while the CompCogs took only 15 minutes.
Results:
CompCogs included the Face test (correct response) and Word and Forms with Short term memory tests (reaction time). CompCogs presented 91.8% sensitivity and 93.6% specificity for the diagnosis of AD using ROC analyses of AD diagnosis probability derived by logistic regression.
Conclusions:
CompCogs showed high validity for AD early diagnosis and, therefore, may be a useful alternative screening instrument.
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