{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T10:14:11Z","timestamp":1774692851428,"version":"3.50.1"},"reference-count":22,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T00:00:00Z","timestamp":1765324800000},"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. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background and aim<\/jats:title>\n                    <jats:p>Osteonecrosis of the femoral head (ONFH) is a degenerative joint disorder that frequently leads to structural collapse and impaired mobility, particularly in older adults. Early detection of associated risk factors is essential for timely intervention. This study aimed to compare the predictive performance of a Random Forest (RF) algorithm and a Logistic Regression (LR) model in identifying key contributors to ONFH in elderly patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective study included 339 patients aged \u2265 75 years who received treatment at Shanxi Bethune Hospital from January 2017 to December 2023, with complete clinical and imaging records. Variables included demographics, bone mineral density, medication and lifestyle history, comorbidities, and radiographic findings. Patients were randomly allocated into training (70%) and validation (30%) cohorts. Predictive models were developed using RF and LR, with performance assessed by accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Both models consistently identified corticosteroid exposure, reduced bone mineral density, prior femoral fractures, and advanced age as major risk factors. The RF model demonstrated superior performance (AUC = 0.896; accuracy = 83.5%; sensitivity = 82.4%; specificity = 84.3%) compared to the LR model (AUC = 0.797; accuracy = 75.0%; sensitivity = 72.0%; specificity = 76.0%). ROC analysis confirmed the RF model\u2019s enhanced discriminative ability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The RF algorithm outperformed traditional logistic regression in predicting ONFH among older adults, highlighting the potential of machine learning techniques to support early risk identification and improve clinical decision-making in orthopedic care.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2025.1640085","type":"journal-article","created":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T06:41:54Z","timestamp":1765348914000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhanced risk prediction of femoral head osteonecrosis in the elderly: a comparative study of random forest and logistic regression models"],"prefix":"10.3389","volume":"12","author":[{"given":"Peng","family":"Shang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"QingQing","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Mu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haijin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junqing","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,12,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"e177","DOI":"10.3346\/jkms.2021.36.e177","article-title":"Osteonecrosis of the femoral head: an updated review of ARCO on pathogenesis, staging and treatment.","volume":"36","author":"Hines","year":"2021","journal-title":"J Korean Med Sci."},{"key":"B2","doi-asserted-by":"publisher","first-page":"1084","DOI":"10.2106\/JBJS.19.01271","article-title":"Nontraumatic osteonecrosis of the femoral head: where do we stand today?: a 5-year update.","volume":"102","author":"Mont","year":"2020","journal-title":"J Bone Joint Surg Am."},{"key":"B3","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1093\/bmb\/ldaa044","article-title":"Osteotomies for avascular necrosis of the femoral head.","volume":"137","author":"Quaranta","year":"2021","journal-title":"Br Med Bull."},{"key":"B4","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1007\/s00776-012-0331-8","article-title":"Logistic regression analysis of factors associated with avascular necrosis of the femoral head following femoral neck fractures in middle-aged and elderly patients.","volume":"18","author":"Ai","year":"2013","journal-title":"J Orthop Sci."},{"key":"B5","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1056\/NEJMcp1513724","article-title":"Clinical practice. 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