{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:50:12Z","timestamp":1792788612946,"version":"4.5.0"},"reference-count":32,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T00:00:00Z","timestamp":1764115200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shaoyang Science and Technology Program Guided Project","award":["2024PT6221"],"award-info":[{"award-number":["2024PT6221"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Arthritis, a common chronic disease encompassing multiple subtypes of osteoarthritis and rheumatoid arthritis, was explored in this study as a risk-related factor based on data from the 2023 U.S. National Health Interview Survey (NHIS). The study included 26,031 participants (6,849 in the arthritis group; 19,182 in the control group), and 21 variables were found to be significantly different between groups by chi-square test. Fourteen key predictors were screened using support vector machine recursive feature elimination (SVM-RFE): age, general health, chronic obstructive pulmonary disease, gender, hypertension, coronary heart disease, body mass index (BMI), cancer, depression, dementia, asthma, diabetes, smoking status, and hepatitis. The column-linear graphical model constructed based on these variables showed excellent predictive performance (AUC\u2009=\u20090.813), the slope of the calibration curve was close to 1 (P\u2009=\u20090.444) indicating high predictive accuracy, and the decision curve analysis showed that its net benefit was better than that of a single predictor. The study demonstrated that the NHIS column-line graph model constructed based on machine learning algorithms can effectively predict the risk of arthritis and provide an important reference for clinical management. The prediction model established in this study provides a theoretical basis for accurate prevention and treatment strategies for arthritis.<\/jats:p>","DOI":"10.1371\/journal.pone.0336018","type":"journal-article","created":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T18:23:51Z","timestamp":1764181431000},"page":"e0336018","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting arthritis risk with machine learning: Insights from the 2023 National Health Interview Survey 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