ArticleHealth care science2024
Explainable machine learning model for pre-frailty risk assessment in community-dwelling older adults.
Article in Health care science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Development and External Validation of an Efficient Deep Learning Model for Lesion Segmentation and Subtyping of Hepatic Echinococcosis in Ultrasound Imaging.Health care science · 2026Article
- Machine Learning-Based Frailty Prediction and Classification in Community-Dwelling Older Adults: A Systematic Review of Validation, Explainability, and Implementation Readiness.Healthcare (Basel, Switzerland) · 2026Review
- Cerebral blood flow alterations and machine learning classification in chronic low back pain using multi-PLD ASL radiomics.Scientific reports · 2026Article
- Explainable machine learning model for pre-frailty risk assessment in community-dwelling older adults.Health care science · 2024Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Frailty in older adults is linked to increased risks and lower quality of life. Pre-frailty, a condition preceding frailty, is intervenable, but its determinants and assessment are challenging. This study aims to develop and validate an explainable machine learning model for pre-frailty risk assessment among community-dwelling older adults. Methods: The study included 3141 adults aged 60 or above from the China Health and Retirement Longitudinal Study. Pre-frailty was characterized by one or two criteria from the physical frailty phenotype scale. We extracted 80 distinct features across seven dimensions to evaluate pre-frailty risk. A model was constructed using recursive feature elimination and a stacking-CatBoost distillation module on 80% of the sample and validated on a separate 20% holdout data set. Results: The study used data from 2508 community-dwelling older adults (mean age, 67.24 years [range, 60-96]; 1215 [48.44%] females) to develop a pre-frailty risk assessment model. We selected 57 predictive features and built a distilled CatBoost model, which achieved the highest discrimination (AUROC: 0.7560 [95% CI: 0.7169, 0.7928]) on the 20% holdout data set. The living city, BMI, and peak expiratory flow (PEF) were the three most significant contributors to pre-frailty risk. Physical and environmental factors were the top 2 impactful feature dimensions. Conclusions: An accurate and interpretable pre-frailty risk assessment framework using state-of-the-art machine learning techniques and explanation methods has been developed. Our framework incorporates a wide range of features and determinants, allowing for a comprehensive and nuanced understanding of pre-frailty risk.
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Registered trials
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