ArticleThe journals of gerontology. Series B, Psychological sciences and social sciences2020
Investigating Predictors of Cognitive Decline Using Machine Learning.
Article in The journals of gerontology. Series B, Psychological sciences and social sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of machine learning in measurement of ageing and geriatric diseases: a systematic review.BMC geriatrics · 2023Pooled it
- Permanent cognitive or physical impairment after transfer from long-term care to acute care: a retrospective cohort study.Age and ageing · 2026Article
- Are we there yet? Estimating the waves of follow-up required for stable effect estimates in cognitive aging research.American journal of epidemiology · 2026Article
- Article
- Development of a predictive model for loss of functional and cognitive abilities in long-term care home residents: a protocol.BMJ open · 2025Article
- A hybrid approach for pattern recognition and interpretation in age-related false memory.Frontiers in psychology · 2025Article
- Prediction of cognitive decline in older breast cancer survivors: the Thinking and Living with Cancer study.JNCI cancer spectrum · 2024Article
- Comparative study of machine learning methods for modeling associations between risk factors and future dementia cases.GeroScience · 2024Article
- Trajectories and influencing factors of cognitive function and physical disability in Chinese older people.Frontiers in public health · 2024Article
- Factors associated with healthy aging in Latin American populations.Nature medicine · 2023Article
- Immediate word recall in cognitive assessment can predict dementia using machine learning techniques.Alzheimer's research & therapy · 2023Article
- Exposure factors associated with dementia among older adults in Iceland: the AGES-Reykjavik study.GeroScience · 2023Article
- Neighbourhood deprivation across eight decades and late-life cognitive function in the Lothian Birth Cohort 1936: a life-course study.Age and ageing · 2023Article
- Cognitive Impairment and Supplemental Nutrition Assistance Program Take-Up Among the Eligible Older Americans.The journals of gerontology. Series B, Psychological sciences and social sciences · 2023Article
- Article
- Machine learning-based prediction of clinical outcomes after first-ever ischemic stroke.Frontiers in neurology · 2023Article
- A risk prediction model based on machine learning for early cognitive impairment in hypertension: Development and validation study.Frontiers in public health · 2023Article
- Associating persistent self-reported cognitive decline with neurocognitive decline in older breast cancer survivors using machine learning: The Thinking and Living with Cancer study.Journal of geriatric oncology · 2022Article
- Machine Learning for Prediction of Cognitive Health in Adults Using Sociodemographic, Neighbourhood Environmental, and Lifestyle Factors.International journal of environmental research and public health · 2022Article
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Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectivesGenetic risks for cognitive decline are not modifiable; however their relative importance compared to modifiable factors is unclear. We used machine learning to evaluate modifiable and genetic risk factors for Alzheimer's disease (AD), to predict cognitive decline.
methodsHealth and Retirement Study participants, aged 65-90 years, with DNA and >2 cognitive evaluations, were included (n = 7,142). Predictors included age, body mass index, gender, education, APOE ε4, cardiovascular, hypertension, diabetes, stroke, neighborhood socioeconomic status (NSES), and AD risk genes. Latent class trajectory analyses of cognitive scores determined the form and number of classes. Random Forests (RF) classification investigated predictors of cognitive trajectories. Performance metrics (accuracy, sensitivity, and specificity) were reported.
resultsThree classes were identified. Discriminating highest from lowest classes produced the best RF performance: accuracy = 78% (1.0%), sensitivity = 75% (1.0%), and specificity = 81% (1.0%). Top ranked predictors were education, age, gender, stroke, NSES, and diabetes, APOE ε4 carrier status, and body mass index (BMI). When discriminating high from medium classes, top predictors were education, age, gender, stroke, diabetes, NSES, and BMI. When discriminating medium from the low classes, education, NSES, age, diabetes, and stroke were top predictors. DISCUSSION: 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.