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Updated: Aug 26, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Comparative analysis of sensitivity and specificity of computer-aided cognitive test in screening mild cognitive
Jing Ma1, Renren Li1, Wei Zhang1
1Department of Neurology, Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
Objectives:
To evaluate the reliability and validity of the computer-aided cognitive test (CACT).
Methods:
219 Subjects of Tongji Hospital's Brain Health cohort (115 cases of Mild Cognitive Impairment (MCI) patients and 104 cases of normal controls) were enrolled, of which 24 cases received a retest after 2 weeks. Finally, the reliability and validity of the scale were tested and analyzed.
Results:
(1) Reliability: (a) the internal consistency reliability of the total score of the scale was 0.645; (b) the retest reliability correlation coefficient of the total score of the scale was 0.900; (c) the Guttman Split-Half coefficient was 0.631; (2) Validity: (a) construct validity analysis showed that the correlation coefficient between each section score was between 0.036 and 0.408, and the correlation coefficient between each section score and the total score was between 0.468 and 0.781; (b) criterion validity analysis showed that the correlation coefficient between the total score of CACT and that of the Mini Mental State Examination (MMSE) was 0.733, and the coefficient between the total score of CACT and that of the basic version of the Montreal Cognitive Assessment (MoCA) was 0.763; (c) the area under the ROC curve of the CACT to distinguish between MCI patients and controls was 0.920, with an optimal diagnostic threshold of 20, a sensitivity of 88.5%, and a specificity of 80.9%.
Conclusion:
The CACT is little influenced by education level. It has good reliability and validity, which can be used for early clinical screening of cognitive dysfunction.
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