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Updated: Jun 1, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Successful aging: definitions and prediction of longevity and conversion to mild cognitive impairment
Selam Negash1, Glenn E Smith, Shane Pankratz
1Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA. negash.selamawit@mayo.edu
Objectives:
To examine alternative models of defining and characterizing successful aging.
Design:
A retrospective cohort study.
Setting:
Olmsted County, MN.
Participants:
Five hundred sixty community-dwelling nondemented adults, aged 65 years and older.
Measurements:
Three models were developed. Each model examined subtests in four cognitive domains: memory, attention/executive function, language, and visuospatial skills. A composite domain score was generated for each of the four domains. In Model 1, a global z score was further generated from the four cognitive domains, and subjects with mean global z score in the top 10% were classified as "successful agers," whereas those in the remaining 90% were classified as "typical agers." In Model 2, subjects with all four domain scores above the 50th percentile were classified as "successful agers." In Model 3, a primary neuropsychological variable was selected from each domain, and subjects whose score remained above - 1 standard deviation compared with norms for young adults were labeled successful agers. Validation tests were conducted to determine the ability of each model to predict survival and conversion to mild cognitive impairment (MCI).
Results:
Model 1 showed 65% lower mortality in successful agers compared with typical agers and also a 25% lower conversion rate to MCI.
Conclusion:
Model 1 was most strongly associated with longevity and cognitive decline; as such, it can be useful in investigating various predictors of successful aging, including plasma level, APOE genotype, and neuroimaging measurements.
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