ArticleBMC public health2022
Characterizing fall risk factors in Belgian older adults through machine learning: a data-driven approach.
Article in BMC public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Outdoor Environmental Risk Factors for Falls and Fear of Falling: A Systematic Review.The Gerontologist · 2025Pooled it
- Are We Missing the Environmental Factors in AI-Based Fall Risk Models?: A Systematic Review.Research square · 2026Article
- Predicting fall risk among older adults with sarcopenia in China using machine learning models: a six-year longitudinal study from CHARLS.BMC geriatrics · 2026Article
- Predicting admission for fall-related injuries in older adults using artificial intelligence: A proof-of-concept study.Geriatrics & gerontology international · 2025Article
- Factors Associated with Falls in Community-Dwelling Older Adults: A Subgroup Analysis from a Telemergency Service.Geriatrics (Basel, Switzerland) · 2024Article
- Volumetric regional MRI and neuropsychological predictors of motor task variability in cognitively unimpaired, Mild Cognitive Impairment, and probable Alzheimer's disease older adults.Experimental gerontology · 2023Article
- Editorial: Neuro-cognition in human movement: from fundamental experiments to bio-inspired innovation.Frontiers in neurologyArticle
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFalls are a major problem associated with ageing. Yet, fall-risk classification models identifying older adults at risk are lacking. Current screening tools show limited predictive validity to differentiate between a low- and high-risk of falling.
objectiveThis study aims at identifying risk factors associated with higher risk of falling by means of a quality-of-life questionnaire incorporating biological, behavioural, environmental and socio-economic factors. These insights can aid the development of a fall-risk classification algorithm identifying community-dwelling older adults at risk of falling.
methodsThe questionnaire was developed by the Belgian Ageing Studies research group of the Vrije Universiteit Brussel and administered to 82,580 older adults for a detailed analysis of risk factors linked to the fall incidence data. Based on previously known risk factors, 139 questions were selected from the questionnaire to include in this study. Included questions were encoded, missing values were dropped, and multicollinearity was assessed. A random forest classifier that learns to predict falls was trained to investigate the importance of each individual feature.
resultsTwenty-four questions were included in the classification-model. Based on the output of the model all factors were associated with the risk of falling of which two were biological risk factors, eight behavioural, 11 socioeconomic and three environmental risk factors. Each of these variables contributed between 4.5 and 6.5% to explaining the risk of falling.
conclusionThe present study identified 24 fall risk factors using machine learning techniques to identify older adults at high risk of falling. Maintaining a mental, physical and socially active lifestyle, reducing vulnerability and feeling satisfied with the living situation contributes to reducing the risk of falling. Further research is warranted to establish an easy-to-use screening tool to be applied in daily practice.
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