Predicting osteoarthritis in adults using statistical data mining and machine learning.
arthritis
machine learning
osteoarthritis
statistical data mining
Journal
Therapeutic advances in musculoskeletal disease
ISSN: 1759-720X
Titre abrégé: Ther Adv Musculoskelet Dis
Pays: England
ID NLM: 101517322
Informations de publication
Date de publication:
2022
2022
Historique:
received:
22
09
2021
accepted:
16
05
2022
entrez:
21
7
2022
pubmed:
22
7
2022
medline:
22
7
2022
Statut:
epublish
Résumé
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. 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. 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 ( Being a female ( 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.
Sections du résumé
Background
UNASSIGNED
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
UNASSIGNED
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
UNASSIGNED
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
UNASSIGNED
Being a female (
Conclusion
UNASSIGNED
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.
Identifiants
pubmed: 35859927
doi: 10.1177/1759720X221104935
pii: 10.1177_1759720X221104935
pmc: PMC9290106
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1759720X221104935Informations de copyright
© The Author(s), 2022.
Déclaration de conflit d'intérêts
Competing interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
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