JAKCalc: A machine-learning approach to rationalized JAK2 testing in patients with elevated hemoglobin levels

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dc.authoridKeklik Karadag, fatma/0000-0001-6078-5944
dc.authoridOzdemir, Taha Resid/0000-0003-4870-6945
dc.authoridOzyilmaz, Berk/0000-0003-2654-3698
dc.authoridKoseoglu, Fatos Dilan/0000-0002-3947-0355
dc.contributor.authorKoseoglu, Fatos Dilan
dc.contributor.authorKaradag, Fatma Keklik
dc.contributor.authorBulbul, Hale
dc.contributor.authorAlici, Erdem Ugur
dc.contributor.authorOzyilmaz, Berk
dc.contributor.authorOzdemir, Taha Resid
dc.date.accessioned2025-03-20T09:50:55Z
dc.date.available2025-03-20T09:50:55Z
dc.date.issued2024
dc.departmentİzmir Bakırçay Üniversitesi
dc.description.abstractThe demand for Janus Kinase-2 (JAK2) testing has been disproportionate to the low yield of positive results, which highlights the need for more discerning test strategies. The aim of this study is to introduce an artificial intelligence application as a more rational approach for testing JAK2 mutations in cases of erythrocytosis. Test results were sourced from samples sent to a tertiary hospital's genetic laboratory between 2017 and 2023, meeting 2016 World Health Organization criteria for JAK2V617F mutation testing. The JAK2 Somatic Mutation Screening Kit was used for genetic testing. Machine learning models were trained and tested using Python programming language. Out of 458 cases, JAK2V617F mutation was identified in 13.3%. There were significant differences in complete blood count parameters between mutation carriers and non-carriers. Various models were trained with data, with the random forest (RF) model demonstrating superior precision, recall, F1-score, accuracy, and area under the receiver operating characteristic, all reaching 100%. Gradient boosting (GB) model also showed high scores. When compared with existing algorithms, the RF and GB models displayed superior performance. The RF and GB models outperformed other methods in accurately identifying and classifying erythrocytosis cases, offering potential reductions in unnecessary testing and costs.
dc.identifier.doi10.1097/MD.0000000000037751
dc.identifier.issn0025-7974
dc.identifier.issn1536-5964
dc.identifier.issue14
dc.identifier.pmid38579024
dc.identifier.scopus2-s2.0-85190078278
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1097/MD.0000000000037751
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2367
dc.identifier.volume103
dc.identifier.wosWOS:001239024400074
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherLippincott Williams & Wilkins
dc.relation.ispartofMedicine
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250319
dc.subjectartificial intelligence
dc.subjectJAK2V617F mutation
dc.subjectmachine learning
dc.subjectpolycythemia vera
dc.titleJAKCalc: A machine-learning approach to rationalized JAK2 testing in patients with elevated hemoglobin levels
dc.typeArticle

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