{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T10:50:00Z","timestamp":1782125400433,"version":"3.54.5"},"reference-count":40,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T00:00:00Z","timestamp":1762214400000},"content-version":"vor","delay-in-days":3,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100018542","name":"Natural Science Foundation of Sichuan Province","doi-asserted-by":"publisher","award":["2024NSFSC0615"],"award-info":[{"award-number":["2024NSFSC0615"]}],"id":[{"id":"10.13039\/501100018542","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J of Rheum Dis"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The objective is to develop a machine learning model that is capable of effectively distinguishing between cancer and non\u2010cancer patients among systemic lupus erythematosus (SLE).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 2811 patients with SLE were included in this study, among which 208 had concurrent cancers. Age, gender, and 95 clinical and laboratory indicators were included. Initially, data preprocessing and feature transformation were conducted. After evaluating feature importance, selected features were included in the subsequent steps for establishing the machine learning model. The optimal model chosen was assessed using a series of metrics to obtain a comprehensive performance evaluation of the model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Through integrated feature selection, features such as Age, C4, WBC (urine), BASO_R, ALP, AST, and C3 were screened out for model construction. Among the various machine learning (ML) models established after parameter optimization, the logistic regression model performed the best. Furthermore, the logistic regression model still showed the best predictive capability by evaluating metrics such as receiver operating characteristic (ROC) curves, precision\u2010recall curve (PRC) plots, as well as model prediction accuracy, F1 score, and recall.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We have developed a convenient and straightforward machine learning model for SLE patients, which can easily and usefully distinguish cancer from non\u2010cancer patients within the SLE patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/1756-185x.70455","type":"journal-article","created":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T18:53:29Z","timestamp":1762282409000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Efficient Machine Learning Pipeline for Distinguishing 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