{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T23:30:56Z","timestamp":1786491056122,"version":"build-2736575974"},"reference-count":27,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Technology and Health Care"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Thalassemia is a hereditary blood disorder characterized by abnormal hemoglobin production. Common diagnostic methods include complete blood count, high-performance liquid chromatography, and hemoglobin electrophoresis. While physicians make the final diagnosis, advancements in artificial intelligence, specifically machine learning (ML) and deep learning, offer significant potential as auxiliary tools and decision support systems to reduce diagnostic errors. This study investigates ML algorithms for classifying thalassemia and its subtypes, including alpha (\u03b1) thalassemia and beta (\n                    <jats:italic toggle=\"yes\">\u03b2<\/jats:italic>\n                    ) thalassemia (minor, intermedia, and major). A synthetic training dataset of 1534 samples was generated based on the statistical properties and correlation structures of real clinical data. The models were then evaluated using an external real-world dataset of 349 patients from the Hematology Department of Atat\u00fcrk University Research Hospital. Support Vector Machines (SVM), Logistic Regression (LR), XGBoost, Artificial Neural Networks (ANN), and a hybrid stacking model named ThalP were implemented. The ThalP model integrates the probability outputs of SVM, LR, and XGBoost through a neural network meta-classifier. Experimental results demonstrate that the proposed ThalP model achieved strong performance on the real clinical dataset with an accuracy of 83.1% and a macro-F1 score of 0.80. These findings indicate that ML-based hybrid models can serve as effective decision-support tools for classifying thalassemia subtypes using routine hematological parameters.\n                  <\/jats:p>","DOI":"10.1177\/09287329261448689","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T08:57:46Z","timestamp":1780304266000},"page":"621-640","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Diagnosis and classification of thalassemia disease using machine learning: Comparative analysis of traditional models and a novel hybrid approach"],"prefix":"10.1177","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8340-5094","authenticated-orcid":false,"given":"Hakan","family":"Tekin","sequence":"first","affiliation":[{"name":"Institute of Science, Department of Computer Engineering, Atat\u00fcrk University, Erzurum, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0685-6999","authenticated-orcid":false,"given":"Ece G\u00fcl\u015fah","family":"Abbaso\u011fullar\u0131","sequence":"additional","affiliation":[{"name":"Institute of Science, Department of Computer Engineering, Atat\u00fcrk University, Erzurum, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5472-3608","authenticated-orcid":false,"given":"Faruk Baturalp","family":"G\u00fcnay","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering, Atat\u00fcrk University, Erzurum, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,6,1]]},"reference":[{"key":"e_1_3_3_2_2","volume-title":"World Health Organization report on thalassemia","author":"World Health Organization","year":"2023","unstructured":"World Health Organization. World Health Organization report on thalassemia. Geneva: WHO, 2023."},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1086\/381402"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.4103\/jfmpc.jfmpc_1215_21"},{"key":"e_1_3_3_5_2","first-page":"127","article-title":"Thalassemia major and consanguinity in Shiraz city, Iran","volume":"21","author":"Asadi-Pooya AA","year":"2004","unstructured":"Asadi-Pooya AA, Doroudchi M. Thalassemia major and consanguinity in Shiraz city, Iran. Turk J Haematol 2004; 21: 127\u2013130.","journal-title":"Turk J Haematol"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/5052435"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cca.2023.117329"},{"key":"e_1_3_3_8_2","doi-asserted-by":"crossref","unstructured":"Wang H Ma C Zhou L. A brief review of machine learning and its application. In:\u00a02009 International conference on information engineering and computer science. Wuhan China: IEEE 2009 pp.1\u20134.","DOI":"10.1109\/ICIECS.2009.5362936"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0933-3657(01)00077-X"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13061173"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-20605-6"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2024.108581"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.33022\/ijcs.v13i3.4035"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrg3920"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13091551"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-22011-8"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2013.08.020"},{"key":"e_1_3_3_18_2","doi-asserted-by":"crossref","unstructured":"Devanath A Akter S Karmaker P et al. Thalassemia Prediction using Machine Learning Approaches. In: 2022 6th International Conference on Computing Methodologies and Communication (ICCMC). Erode India: IEEE 2022 pp.7\u20138.","DOI":"10.1109\/ICCMC53470.2022.9753833"},{"key":"e_1_3_3_19_2","first-page":"4468842","article-title":"Fuzzy-based fusion model for \u03b2-thalassemia carriers prediction using machine learning technique","volume":"2024","author":"Ibrahim M","year":"2024","unstructured":"Ibrahim M, Abbas S, Fatima A, et al. Fuzzy-based fusion model for \u03b2-thalassemia carriers prediction using machine learning technique. Adv Fuzzy Syst 2024; 2024: 4468842.","journal-title":"Adv Fuzzy Syst"},{"key":"e_1_3_3_20_2","doi-asserted-by":"crossref","unstructured":"Farzaliyev E Saihood Q Sonu\u00e7 E. \u00c7ocuklarda anemi hastal\u0131\u011f\u0131n\u0131n te\u015fhisinde topluluk \u00d6\u011frenme y\u00f6ntemlerinin kullan\u0131lmas\u0131. In:\u00a01st International conference on recent academic studies. 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