{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T07:46:54Z","timestamp":1773560814671,"version":"3.50.1"},"reference-count":26,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T00:00:00Z","timestamp":1741046400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>This study aims to develop a predictive model to assess the likelihood of psychological disorders in patients with ankylosing spondylitis (AS) and to explore the relationships between different factors and psychological disorders.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Patients were randomly divided into training and test sets in an 8:2 ratio. The Boruta algorithm was applied to select predictive factors, and a multi-label classification learning algorithm based on association rules (AR) was developed. Models were constructed using Random Forest (RF), K-Nearest Neighbor (KNN), RF-AR, and KNN-AR, and their performance was assessed through receiver operating characteristic (ROC) curves on the test set.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 513 AS patients were included, with 410 in the training set and 103 in the test set. The Boruta algorithm identified five key variables for the model: fatigue, ASAS-HI score, disease duration, disease activity, and BMI. The RF-AR model performed best, with an accuracy of 0.89\u202f\u00b1\u202f0.06, recall of 0.78\u202f\u00b1\u202f0.1, F1-score of 0.86\u202f\u00b1\u202f0.08, Hamming loss of 0.05\u202f\u00b1\u202f0.03, and a Jaccard similarity coefficient of 0.75\u202f\u00b1\u202f0.12. The area under the curve (AUC) for the training set was 0.94.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study developed a predictive model for assessing the risk of psychological disorders in AS patients. The model effectively captures the presence of psychological disorders, providing clinicians with valuable insights for adjusting treatment strategies.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fpubh.2025.1497955","type":"journal-article","created":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T05:19:34Z","timestamp":1741065574000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Prediction model for psychological disorders in ankylosing spondylitis patients based on multi-label classification"],"prefix":"10.3389","volume":"13","author":[{"given":"Kun","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifan","family":"Gong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tiantian","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinning","family":"Qu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaxiu","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongxiao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,3,4]]},"reference":[{"key":"ref1","first-page":"1694","article-title":"Defining spinal mobility in ankylosing spondylitis (AS). 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