Investigating Predictors of Cognitive Decline Using Machine Learning
Bibliographic Data
The combination of latent trajectories and RF classification techniques suggested that nongenetic factors contribute more to cognitive decline than genetic factors. Education was the most relevant predictor for discrimination
Body mass index · Cognition · Cognitive decline · Dementia · Diabetes mellitus · Disease · Environmental health · Latent class model · Machine learning · Population · Psychiatry · Socioeconomic status · Stroke (engine · Computer Science · Dementia and Cognitive Impairment Research · Health, Environment, Cognitive Aging · Medicine · Nutritional Studies and Diet · Psychology · Clinical Psychology · Demography · Endocrinology · Gerontology · Internal Medicine
Cognitive Impairment and Supplemental Nutrition Assistance Program Take-Up Among the Eligible Older Americans
Trajectories and influencing factors of cognitive function and physical disability in Chinese older people
A risk prediction model based on machine learning for early cognitive impairment in hypertension
Effects of neighborhood amenities, services, and built infrastructure on cognitive health
Trajectories of informal care intensity among the oldest-old Chinese
Machine Learning for Prediction of Cognitive Health in Adults Using Sociodemographic, Neighbourhood Environmental, and Lifestyle Factors
A Tale of Two Cities
Key Predictors of Generativity in Adulthood
An introduction to recursive partitioning
Summary of the evidence on modifiable risk factors for cognitive decline and dementia
Cognitive reserve in ageing and Alzheimer's disease
Influence of Education and Occupation on the Incidence of Alzheimer's Disease
Potential for primary prevention of Alzheimer's disease
Conditional variable importance for random forests
Neighborhood Deprivation, Individual Socioeconomic Status, and Cognitive Function in Older People
Random Forests
“Mini-mental state”
Constructing a Time-Invariant Measure of the Socio-economic Status of U.S. Census Tracts
Memory Resilience to Alzheimer’s Genetic Risk
Feature Selection Methods for Optimal Design of Studies for Developmental Inquiry
Neighborhood Socioeconomic Status and Cognitive Function in Women
| Unique citing works | 8 |
|---|---|
| Citations per year | 1.33 |
| Citation span | 2020 - 2026 (7) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 8 |