{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T17:09:58Z","timestamp":1793293798404,"version":"4.5.0"},"reference-count":40,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:00:00Z","timestamp":1760486400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:00:00Z","timestamp":1760486400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Lab Hematology"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Anemia is a significant global health concern, with hypochromic microcytic anemia being the most common type. Among its causes, \u03b2\u2010thalassemia trait (\u03b2\u2010TT) and iron deficiency anemia (IDA) share similar hematological features, making differentiation challenging. We aimed to develop machine learning (ML) models using routine red blood cell (RBC) indices to distinguish \u03b2\u2010TT, IDA, and healthy cases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 8106 individuals (3378 healthy, 2696 IDA, 2032 \u03b2\u2010TT) were included in this study. Six RBC parameters\u2014RBC count, hemoglobin (HGB), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), MCH concentration (MCHC), and RBC distribution width (RDW\u2010CV)\u2014were used to train eXtreme gradient boosting (XGB), random forest (RF), and neural network (NN) models. The dataset was split into training (70%) and testing (30%) sets, with feature importance assessed via the Boruta algorithm. All statistical analyses were performed using R version 4.3.1 Statistical Language.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>All models achieved high accuracy (&gt;\u200997%), with RF demonstrating superior performance (97.86% accuracy, 99.71% AUC). The most significant features contributing to the models are MCH for the XGB algorithm, MCV for the RF algorithm, and HGB for the NN algorithm.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our findings demonstrate that ML\u2010based models could offer a promising tool for improving \u03b2\u2010TT detection, optimizing clinical workflows, and enhancing resource utilization in hematology.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/ijlh.70017","type":"journal-article","created":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T06:54:41Z","timestamp":1760597681000},"page":"211-219","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Machine Learning\u2010Based Prediction of \u03b2\u2010Thalassemia Trait Using Red Blood Cell Indices"],"prefix":"10.1111","volume":"48","author":[{"given":"Esra","family":"Payda\u015f Hataysal","sequence":"first","affiliation":[{"name":"Department of Biochemistry G\u00f6ztepe Prof. Dr. S\u00fcleyman Yal\u00e7\u0131n Training and Research Hospital  Istanbul Turkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muslu Kaz\u0131m","family":"K\u00f6rez","sequence":"additional","affiliation":[{"name":"Department of Biostatistics Selcuk University Faculty of Medicine  Konya Turkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,10,15]]},"reference":[{"issue":"3","key":"e_1_2_10_2_1","first-page":"272","article-title":"Alpha\u2010 and Beta\u2010Thalassemia: Rapid Evidence Review","volume":"105","author":"Baird D. 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