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Updated: Apr 19, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
MCI of different etiologies differ on the Cognitive Assessment Battery
O Nyström1, A Wallin1, A Nordlund1
1Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden.
Objectives:
The study aims to investigate whether patients with mild cognitive impairment (MCI) who have significant vascular disease (MCI-vas) differ from those with no significant vascular disease (MCI-nov) in terms of cognitive profile when assessed with the cognitive assessment battery (CAB).
Materials And Methods:
Seventy patients clinically diagnosed with MCI were included in the study, 32 were classified as MCI-vas, and 38 as MCI-nov, together with 40 healthy controls. CAB consists of six short tests measuring speed and attention, memory, visuospatial functions, language, and executive functions.
Results:
The healthy controls performed better than both MCI groups on CAB. MCI-vas patients were significantly older and had fewer years of education than MCI-nov patients. When adjusted for age and education, MCI-vas performed significantly worse than MCI-nov on memory, language, and executive tests.
Conclusions:
The results suggest that CAB can differentiate between MCI patients with and without vascular disease and that their cognitive profiles differ. Furthermore, CAB classified the patients as vascular and non-vascular MCI with good sensitivity and specificity.
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