ArticleThe journals of gerontology. Series A, Biological sciences and medical sciences2023
Machine Learning Models to Predict Future Frailty in Community-Dwelling Middle-Aged and Older Adults: The ELSA Cohort Study.
Article in The journals of gerontology. Series A, Biological sciences and medical sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 30 citations in OpenAlex.
- An explainable online frailty prediction model for community-dwelling older adults based on machine learning algorithms: a cross-sectional study based on retrospective health data.Annals of medicine · 2026Article
- The Technologies, Applicability, and Trade-Offs of AI in Palliative Care for Older Adults: Scoping Review.Journal of medical Internet research · 2026Article
- Life course shaping of allostatic load across 17 countries: Evidence from England, the U.S., Europe, China and Indonesia.medRxiv : the preprint server for health sciences · 2026Article
- Interpretable machine learning framework for frailty risk prediction using NHANES 2007-2018: A cross-sectional study.Medicine · 2026Article
- Machine learning models to predict the recurrence of common bile duct stones after ERCP.BMC gastroenterology · 2026Article
- ProtFI, an efficient frailty-trained proteomics-based biomarker of aging, robustly predicts age-related decline.Cell reports methods · 2026Article
- Machine Learning-Driven Personalized Risk Prediction: Developing an Explainable Sarcopenia Model for Older European Adults with Arthritis.Journal of clinical medicine · 2026Article
- Machine learning algorithms to predict frailty in older adults in China: a cross-sectional study.BMC geriatrics · 2025Article
- The incremental predictive value of biological aging indicators for cognitive impairment in older adults: a longitudinal analysis on the Mr. OS & Ms. OS cohort.Alzheimer's research & therapy · 2025Article
- Clinical and environmental factors, functional status, and multimorbidity-stratifying progression and prognosis of multimorbidity, frailty, and disability: the Age-It Research Program.The journals of gerontology. Series B, Psychological sciences and social sciences · 2025Article
- Development of Prediction Models for Healthy Ageing in Community-Dwelling Middle-Aged and Older Adults: A Longitudinal Study Using Machine Learning.Journal of the American Medical Directors Association · 2025Article
- Application of machine learning models for predicting depression among older adults with non-communicable diseases in India.Scientific reports · 2025Article
- Examination of Social Participation in Older Adults Undergoing Frailty Health Checkups Using Deep Learning Models.Geriatrics (Basel, Switzerland) · 2025Article
- Development and multi-cohort validation of a machine learning-based simplified frailty assessment tool for clinical risk prediction.Journal of translational medicine · 2025Article
- Using machine-learning to identify differences in the association between blood-based biomarkers and later life health across race and ethnicity.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025Article
- Frailty in older adults: A systematic review of risk factors and early intervention pathways.Intractable & rare diseases research · 2025Review
- Growing up in poverty, growing old in frailty: the life course shaping of health in the United States, England and Europe-a prospective and retrospective study.Scientific reports · 2025Article
- Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study.JMIR aging · 2025Article
- Does Allostatic Load in 50-89-Year-Olds Predict the Development of Frailty? Evidence From a National Longitudinal Study Over 12 Years.Stress and health : journal of the International Society for the Investigation of Stress · 2025Article
- Eye Movement Characteristics for Predicting a Transition to Psychosis: Longitudinal Changes and Implications.Schizophrenia bulletin · 2025Observational
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundMachine learning (ML) models can be used to predict future frailty in the community setting. However, outcome variables for epidemiologic data sets such as frailty usually have an imbalance between categories, that is, there are far fewer individuals classified as frail than as nonfrail, adversely affecting the performance of ML models when predicting the syndrome.
methodsA retrospective cohort study with participants (50 years or older) from the English Longitudinal Study of Ageing who were nonfrail at baseline (2008-2009) and reassessed for the frailty phenotype at 4-year follow-up (2012-2013). Social, clinical, and psychosocial baseline predictors were selected to predict frailty at follow-up in ML models (Logistic Regression, Random Forest [RF], Support Vector Machine, Neural Network, K-nearest neighbor, and Naive Bayes classifier).
resultsOf all the 4 378 nonfrail participants at baseline, 347 became frail at follow-up. The proposed combined oversampling and undersampling method to adjust imbalanced data improved the performance of the models, and RF had the best performance, with areas under the receiver-operating characteristic curve and the precision-recall curve of 0.92 and 0.97, respectively, specificity of 0.83, sensitivity of 0.88, and balanced accuracy of 85.5% for balanced data. Age, chair-rise test, household wealth, balance problems, and self-rated health were the most important frailty predictors in most of the models trained with balanced data.
conclusionsML proved useful in identifying individuals who became frail over time, and this result was made possible by balancing the data set. This study highlighted factors that may be useful in the early detection of frailty.
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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.