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Updated: Mar 9, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
A cross-sectional study to evaluate metabolic and demographic factors affecting cognitive function among low educated
Feride Alakus1, Serife A Helvaci, Mustafa Temizel
1Internal Medicine Clinics, Okmeydani Training and Research Hospital, Istanbul, Turkey.
Objective:
To evaluate factors affecting cognitive function in internal medicine outpatients.
Methods:
A total of 130 consecutive outpatients aged 50-80 years old were included in this cross-sectional study conducted at Okmeydani Training and Research Hospital, Istanbul, Turkey between March and May 2013. Cognitive function was evaluated via Standardized Mini-Mental State Examination (SMMSE) scores. Logistic regression analysis was performed to determine factors predicting poor cognitive function.
Results:
Mild-to-moderate cognitive impairment was noted in 39.2% of the patients. Median (interquartile range) total SMMSE scores were significantly higher in patients aged 60 years (27.0 (2.0) vs. 25.0 (5.0), p=0.000). Multivariate linear regression analyses revealed female gender (B, -1.27; 95% CI, -2.36 to -0.18; p=0.023) and aging (B, -0.20; 95% CI, -0.26 to -0.14; p<0.001) to result in a significant decrease in the total SMMSE scores.
Conclusion:
Mild-to-moderate cognitive impairment was observed in 39.2% of internal medicine outpatients. Old age and female gender were significant predictors of lower total SMMSE scores. Furthermore, besides for high language scores in hypertensive patients on combined therapy, no significant impact of hypertension or obesity was observed on the SMMSE scores.
