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Updated: May 28, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Mild cognitive impairment: an epidemiological perspective
1Institut National de la Santé et de la Recherche Médicale (INSERM), E361 Epidemiology of Nervous System Pathologies, La Colombière Hospital, Montpellier, France.
Abstract:
Mild cognitive impairment (MCI) refers to cognitive impairment that is assumed to be due to pathological central nervous system processes, but which interacts with normal aging-related changes. Epidemiological studies conducted in the general population have been able to examine more heterogeneous forms of this disorder than clinical studies, and have also been able to provide early estimations of population incidence and prevalence. Large differences in case identification procedures and sampling methods have led to considerable divergence in the rates of prevalence reported, which ranged from 1% to 29%. Suggested improvements in the definition of MCI have led to an upward adjustment of prevalence rates in most studies, giving between 5% and 29%. Incidence is estimated as 8 to 58 new cases per thousand persons per year, and the probability of conversion from MCI to dementia is estimated at around 15%. The principal risk factors that have been identified so far for MCI using regression models applied to general population data are age, education, race, medicated hypertension, infarcts, white matter lesions, depression, and apolipoprotein E4 (AP0E-4J allele. An etiological model derived from these studies indicates possible intervention points for future therapeutic strategies at the level of both clinical intervention and environmental exposure. There is, however, a clear need for epidemiological studies that take into account a broader range of risk factors than those studied to date, which have focused principally on known risk factors for dementia.
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