{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T01:25:47Z","timestamp":1771637147255,"version":"3.50.1"},"reference-count":16,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T00:00:00Z","timestamp":1635379200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Lab Hematology"],"published-print":{"date-parts":[[2022,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Introduction<\/jats:title><jats:p>The differential diagnosis of anemia is an important issue for hematology laboratories. We aimed at investigating the performance of a powerful computer\u2010based model to aid diagnosis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and methods<\/jats:title><jats:p>Our work presents a new feature selection\u2010based automated disease diagnosis model. To create a testbed, a new corpus is collected retrospectively. Our data sets contain beta thalassemia trait, iron deficiency anemia, and healthy groups. Our presented automated ailment classification model consists iterative chi2 (IChi2) feature selection and classification phases. The used data set includes 25 features, and IChi2 selects the 20 most valuable of them. These are forwarded to 24 traditional classifiers.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In this work, two data sets have been used to test our proposal. In the classification phase of this model, 24 shallow classifiers have been used and the best accurate classifiers are Medium Gaussian Support Vector Machine (MGSVM) and Coarse Tree (CT) for the first and second data sets, respectively. These classifiers have been attained 97.48% and 99.73% classification accuracies using the first and second data sets, consecutively. These results are calculated using 10\u2010fold cross\u2010validation. Moreover, hold\u2010out validation has been used in this work, and the results are given in the experiments.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Our results denoted the success of IChi2\u2010based classification model for diagnosis on the laboratory data set. We have found a new and robust model to differentiate iron deficiency anemia and beta thalassemia trait. This model may be beneficial for rational laboratory use.<\/jats:p><\/jats:sec>","DOI":"10.1111\/ijlh.13745","type":"journal-article","created":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T12:46:13Z","timestamp":1635425173000},"page":"430-436","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Automated differential diagnosis method for iron deficiency anemia and beta thalassemia trait based on iterative Chi2 feature selector"],"prefix":"10.1111","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6664-4568","authenticated-orcid":false,"given":"Mehmet","family":"Erten","sequence":"first","affiliation":[{"name":"Laboratory of Medical Biochemistry Public Health Lab Malatya Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Turker","family":"Tuncer","sequence":"additional","affiliation":[{"name":"Department of Digital Forensics Engineering Technology Faculty Firat University Elazig Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,10,28]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2014-01-548776"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/457834"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.4081\/hr.2015.5843"},{"issue":"138","key":"e_1_2_8_5_1","first-page":"109611","article-title":"Discrimination of \u03b2\u2010thalassemia and iron deficiency anemia through extreme learning machine and regularized extreme learning machine based decision support system","volume":"2020","author":"\u00c7il B","year":"2019","journal-title":"Med Hypotheses"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12288-020-01373-x"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-019-0929-2"},{"key":"e_1_2_8_8_1","first-page":"388","volume-title":"Proceedings of 7th IEEE International Conference On Tools With Artificial Intelligence","author":"Liu H","year":"1995"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-78189-1_8"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/SIU.2017.7960534"},{"issue":"4","key":"e_1_2_8_11_1","first-page":"326","article-title":"Automated diagnosis of iron deficiency anemia and thalassemia by data mining techniques","volume":"17","author":"Hasani M","year":"2017","journal-title":"Int J Comput Sci Netw Secur"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.5455\/aim.2019.27.78-84"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2015.112"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-011-9668-3"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1177\/0272989X0202200102"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.4084\/mjhid.2015.022"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.5455\/medscience.2021.03.090"}],"container-title":["International Journal of Laboratory Hematology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/ijlh.13745","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/ijlh.13745","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/ijlh.13745","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T22:53:00Z","timestamp":1693003980000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/ijlh.13745"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,28]]},"references-count":16,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["10.1111\/ijlh.13745"],"URL":"https:\/\/doi.org\/10.1111\/ijlh.13745","archive":["Portico"],"relation":{},"ISSN":["1751-5521","1751-553X"],"issn-type":[{"value":"1751-5521","type":"print"},{"value":"1751-553X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,28]]},"assertion":[{"value":"2021-05-27","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-10-08","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-10-28","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}