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Updated: Jan 5, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
[Clinical and demographic predictors of mild cognitive impairment - cross-sectional study]
Anna Rajtar-Zembaty1, Andrzej Sałakowski2, Jakub Rajtar-Zembaty3
1Zakład Psychologii Lekarskiej, Katedra Psychiatrii, Uniwersytet Jagielloński, Collegium Medicum w Krakowie, Kraków, Polska.
Objective:
Introduction: Cognitive limitations are a serious health and social problem, which concerns elderly people. Effective prevention and treatment of cognitive dysfunction is one of the challenges of modern medicine. There is not enough consistent data in the literature to indicate to indicate the relationship between various clinical and demographic factors with cognitive functioning in different age ranges. The aim: To analyze clinical and demographic predictors of mild cognitive impairment by age group.
Patients And Methods:
Material and methods: The analysis included 817 participants (669 with normal cognitive function and 148 people with MCI). The evaluation of the level of depressive symptoms was measured by the Short Form Geriatric Depression Scale. All participants were screened for cognitive functioning using the Mini-Mental State Examination and Addenbrooke's Cognitive Examination-III. Different cognitive domains were evaluated with different neuropsychological tools: the Rey Auditory Verbal Learning test, Clock Drawing test, Verbal Fluency test, Digit Span Test and Trail Making test.
Results:
Results: It has been shown that independently associated with MCI ware age (OR = 1.09, 95% CI: 1.05-1.13) and level of education (OR = 0.75, 95% CI: 0.69-0.81). Depending on the age, it turned out that in the younger MCI group, age, education and depression were significant, and the age and level of education were significant in the older MCI group.
Conclusion:
Conclusions: The identification of cognitive dysfunctions is an important element of the diagnostic and therapeutic process.
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