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Machine Learning Models to Predict Future Frailty in Community-dwelling Middle-aged and Older Adults: the ELSA cohort study

da Cunha Leme, Daniel Eduardo; De Oliveira, Cesar; (2023) Machine Learning Models to Predict Future Frailty in Community-dwelling Middle-aged and Older Adults: the ELSA cohort study. The Journals of Gerontology, Series A: Biological Sciences and Medical Sciences 10.1093/gerona/glad127. (In press). Green open access

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Abstract

BACKGROUND: Machine learning (ML) models can be used to predict future frailty in the community setting. However, outcome variables for epidemiologic datasets such as frailty usually have an imbalance between categories, i.e., there are far fewer individuals classified as frail than as non-frail, adversely affecting the performance of ML models when predicting the syndrome. METHODS: A retrospective cohort study with participants (50 years or older) from the English Longitudinal Study of Ageing who were non-frail at baseline (2008-2009) and reassessed for the frailty phenotype at four-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 neighbour, and Naive Bayes classifier). RESULTS: Of all the 4378 non-frail 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. CONCLUSION: Machine learning proved useful in identifying individuals who became frail over time, and this result was made possible by balancing the dataset. This study highlighted factors that may be useful in the early detection of frailty.

Type: Article
Title: Machine Learning Models to Predict Future Frailty in Community-dwelling Middle-aged and Older Adults: the ELSA cohort study
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1093/gerona/glad127
Publisher version: https://doi.org/10.1093/gerona/glad127
Language: English
Additional information: This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
Keywords: Artificial intelligence, Frailty, Outcome, Risk factors
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Epidemiology and Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Epidemiology and Health > Epidemiology and Public Health
URI: https://discovery.ucl.ac.uk/id/eprint/10174227
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