ArticleTherapeutic advances in musculoskeletal disease2022
Predicting osteoarthritis in adults using statistical data mining and machine learning.
Article in Therapeutic advances in musculoskeletal disease, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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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
8 citing papers in PubMed, 14 citations in OpenAlex.
- Trial
- Knee Osteoarthritis Diagnosis: Future and Perspectives.Biomedicines · 2025Review
- Constructing machine learning-based risk prediction model for osteoarthritis in population aged 45 and above: NHANES 2011-2018.Scientific reports · 2025Article
- Tuina therapy alleviates knee osteoarthritis by modulating PI3K/AKT/mTOR-mediated autophagy: an integrated machine learning andFrontiers in immunology · 2025Article
- Chinese Massage (Tuina) Attenuates Knee Osteoarthritis by Modulating Autophagy-Related Cytokines: A Multidisciplinary Methodological Investigation.Journal of inflammation research · 2025Article
- Machine Learning Models for Prediction of Progression of Knee Osteoarthritis: A Comprehensive Analysis.Journal of pharmacy & bioallied sciences · 2024Article
- Article
- Osteo-NeT: An Automated System for Predicting Knee Osteoarthritis from X-ray Images Using Transfer-Learning-Based Neural Networks Approach.Healthcare (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
9 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Osteoarthritis (OA) has traditionally been considered a disease of older adults (⩾65 years old), but it may appear in younger adults. However, the risk factors for OA in younger adults need to be further evaluated. Objectives: To develop a prediction model for identifying risk factors of OA in subjects aged 20-50 years and compare the performance of different machine learning models. Methods: We included data from 52,512 participants of the National Health and Nutrition Examination Survey; of those, we analyzed only subjects aged 20-50 years ( Results: Being a female ( Conclusion: Sex (female), age (older), smoking (yes), body mass index (higher), blood pressure (high), race/ethnicity, and physical and mental limitations are risk factors for having OA in adults aged 20-50 years. The best predictive performance was achieved using DNN algorithms.
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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.