Implementation a various types of machine learning approaches for biomedical datasets based on sickle cell disorder

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dc.contributor.advisorUçan, Osman Nuri
dc.contributor.authorDheyab, Hamid Falah
dc.date.accessioned2022-07-04T11:54:06Z
dc.date.available2022-07-04T11:54:06Z
dc.date.issued2020en_US
dc.date.submitted2020
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Bilişim Teknolojileri Ana Bilim Dalıen_US
dc.description.abstractThis study presents implementation a various kinds of machine learning models to classify the dataset of sickle cell patients. Artificial intelligence techniques have served to strengthen the medical field in solving its problems and providing rapid technical methods with high efficiency instead of traditional methods that can be subject to many problems in diagnosis and to determine the appropriate treatment. The main objective of this study to obtain a highly qualified classifier capable of determining the suitable dose of the SCD patients from 9 classes. Through examining the techniques used in our experiment based on performance evaluation metrics and making sure that each model performs. We applied numerous models of machine learning classifiers to examine the sickle cell dataset based on the performance evaluation metrics. The outcomes obtained from all classifiers, show that the Naïve Bayes Classifier obtained poor results compared to other classifiers. While Levenberg-Marquardt Neural Network during the training phase obtained the highest performance and accuracy of 0.935222, AUC 0.963889. The test phase obtained an accuracy of 0.846444, AUC 0.871889.en_US
dc.identifier.citationDheyab, Hamid Falah. (2020). Implementation a various types of machine learning approaches for biomedical datasets based on sickle cell disorder. (Yayınlanmamış yüksek lisans tezi). Altınbaş Üniversitesi, Lisansüstü Eğitim Enstitüsü, İstanbul.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/2605
dc.identifier.yoktezid672357
dc.institutionauthorDheyab, Hamid Falah
dc.language.isoen
dc.publisherAltınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsüen_US
dc.relation.publicationcategoryTezen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMachine-Learning Classifiersen_US
dc.subjectSickle Cell Disorderen_US
dc.subjectSCD Date Setsen_US
dc.subjectPerformance Evaluationen_US
dc.titleImplementation a various types of machine learning approaches for biomedical datasets based on sickle cell disorder
dc.typeMaster Thesis

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