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Updated: Jun 21, 2026
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
[Predictors of cognitive decline in the elderly]
Angel G López1, M Dolores Calero
1Departamento de Personalidad, Evaluación y Tratamiento Psicológico, Facultad de Psicología, Universidad de Granada, Granada, España. angellpd@gmail.com
Revista Espanola De Geriatria Y Gerontologia
|July 15, 2009
Summary
Identifying predictors of cognitive decline is crucial for early intervention in the growing elderly population. Factors like age, medical history, and lifestyle choices significantly influence cognitive health.
Area of Science:
- Gerontology and Neuroscience
- Public Health
Context:
- The global elderly population is rapidly expanding.
- Cognitive decline and dementia pose significant personal, familial, economic, and healthcare challenges.
Purpose:
- To identify potential predictors of cognitive decline in older adults.
- To enable early prediction and intervention strategies for cognitive impairment.
Summary:
- Numerous factors are associated with cognitive decline, including age, sex, education, family history of dementia, memory issues, hypertension, diabetes, sensory-motor deficits, cerebral hypometabolism, reduced hippocampal volume, apolipoprotein e4 alleles, low cognitive task performance, associative learning deficits, naming difficulties, low cognitive plasticity, depression, low literacy, poor general cognition, reduced social and physical activity, lack of social support, medication use, and sleep disturbances.
- These predictors are vital for forecasting cognitive decline in advanced age.
Impact:
- Early identification of at-risk individuals allows for timely interventions.
- Proactive management can mitigate the severe consequences of cognitive impairment on individuals and society.
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