Survival Rate Prediction of Blood Cancer (Leukemia) Patients Using Machine Learning Algorithms

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dc.contributor.authorZarish
dc.contributor.authorWajid, Bilal
dc.contributor.authorRashid, Umar
dc.contributor.authorZahid, Sajida
dc.contributor.authorAnwar, Faria
dc.contributor.authorAwan, Fahim Gohar
dc.contributor.authorAnwar, Abdul Rauf
dc.contributor.authorWajid, Imran
dc.date.accessioned2023-10-27T12:38:45Z
dc.date.available2023-10-27T12:38:45Z
dc.date.issued2022en_US
dc.departmentEnstitüler, Sosyal Bilimler Enstitüsü, İşletme Ana Bilim Dalıen_US
dc.description.abstractSurvival rate prediction for medical diseases is a complex task that requires high precision. With a low survival rate among reported patients, leukemia is a type of cancer of blood which is caused by the abnormal growth of white blood cells. It is critical to numerically evaluate the rate of survivability of patients suffering from leukemia. To this end, this paper employs a comprehensive database, namely Surveillance, Epidemiology, and End Results (SEER) maintained by The National Cancer Institute in MD, USA, to construct a survivability model for leukemia patients. To accurately predict the survival months of the patients, we develop a multi-class classification problem by binning the target variable into four bins. The resulting accuracy is improved by utilizing a multi-tier classification framework. Although, the final numerical results hold significance from biological viewpoint, it is recommended that a clinically relevant model be drawn with caution.en_US
dc.identifier.doi10.1109/ETECTE55893.2022.10007402en_US
dc.identifier.scopus2-s2.0-85147140457en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/6840
dc.identifier.urihttps://doi.org/10.1109/ETECTE55893.2022.10007402
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2022 International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering, ETECTE 2022 - Proceedingsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectBlood cancer, leukemia, lymphoma, machine learning, survival months, SEERen_US
dc.titleSurvival Rate Prediction of Blood Cancer (Leukemia) Patients Using Machine Learning Algorithmsen_US
dc.typeConference Objecten_US

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