{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T20:06:04Z","timestamp":1790280364970,"version":"4.1.0"},"reference-count":39,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T00:00:00Z","timestamp":1744156800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006545","name":"National Institute on Minority Health and Health Disparities","doi-asserted-by":"publisher","award":["R21MD013681"],"award-info":[{"award-number":["R21MD013681"]}],"id":[{"id":"10.13039\/100006545","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["R01AG080017"],"award-info":[{"award-number":["R01AG080017"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["P20GM121325"],"award-info":[{"award-number":["P20GM121325"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosdigitalhealth.org"],"crossmark-restriction":false},"short-container-title":["PLOS Digit Health"],"abstract":"<jats:p>Genetic factors contribute to 60-70% of the variability in rheumatoid arthritis (RA). However, few studies have used genetic variants to predict RA risk. This study aimed to enhance RA risk prediction by leveraging single nucleotide polymorphisms (SNPs) through machine-learning algorithms, utilizing Women\u2019s Health Initiative data. We developed four predictive models: 1) based on common RA risk factors, 2) model 1 incorporating polygenic risk scores (PRS) with principal components, 3) model 1 and SNPs after feature reduction, and 4) model 1 and SNPs with kernel principal component analysis. Each model was assessed using logistic regression (LR), random forest (RF), eXtreme Gradient Boosting (XGBoost), and support vector machine (SVM). Performance metrics included the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive and negative predictive values (PPV and NPV), and F1-score. The fourth model, integrating SNPs with XGBoost, outperformed all other models. In addition, the XGBoost model that combines genomic data with conventional phenotypic predictors significantly enhanced predictive accuracy, achieving the highest AUC of 0.90 and an F1 score of 0.83. The DeLong test confirmed significant differences in AUC between this model and the others (p-values &lt; 0.0001), particularly highlighting its efficacy in utilizing complex genetic information. These findings emphasize the advantage of combining in-depth genomic data with advanced machine learning for RA risk prediction. The most robust performance of the XGBoost model, which integrated both conventional risk factors and individual SNPs, demonstrates its potential as a tool in personalized medicine for complex diseases like RA. This approach offers a more nuanced and effective RA risk assessment strategy, underscoring the need for further studies to extend broader applications.<\/jats:p>","DOI":"10.1371\/journal.pdig.0000790","type":"journal-article","created":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T13:27:41Z","timestamp":1744205261000},"page":"e0000790","update-policy":"https:\/\/doi.org\/10.1371\/journal.pdig.corrections_policy","source":"Crossref","is-referenced-by-count":6,"title":["Using machine learning and single nucleotide polymorphisms for improving rheumatoid arthritis risk Prediction in postmenopausal women"],"prefix":"10.1371","volume":"4","author":[{"given":"Yingke","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4679-8903","authenticated-orcid":true,"given":"Qing","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,4,9]]},"reference":[{"issue":"7","key":"pdig.0000790.ref001","doi-asserted-by":"crossref","first-page":"1316","DOI":"10.1136\/annrheumdis-2013-204627","article-title":"The global burden of rheumatoid arthritis: estimates from the global burden of disease 2010 study","volume":"73","author":"M Cross","year":"2014","journal-title":"Ann Rheum Dis"},{"issue":"13","key":"pdig.0000790.ref002","doi-asserted-by":"crossref","first-page":"1360","DOI":"10.1001\/jama.2018.13103","article-title":"Diagnosis and Management of Rheumatoid Arthritis: A Review","volume":"320","author":"D Aletaha","year":"2018","journal-title":"JAMA"},{"issue":"10","key":"pdig.0000790.ref003","doi-asserted-by":"crossref","first-page":"e594","DOI":"10.1016\/S2665-9913(23)00211-4","article-title":"Global, regional, and national burden of rheumatoid arthritis, 1990-2020, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021","volume":"5","author":"GBD 2021 Rheumatoid Arthritis Collaborators","year":"2023","journal-title":"Lancet Rheumatol"},{"issue":"4","key":"pdig.0000790.ref004","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1002\/art.23538","article-title":"Contemporary prevalence and incidence of work disability associated with rheumatoid arthritis in the US","volume":"59","author":"S Allaire","year":"2008","journal-title":"Arthritis Rheum"},{"key":"pdig.0000790.ref005","article-title":"Arthritis. 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