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

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dc.contributor.authorDheyab, Hamid Falah
dc.contributor.authorUçan, Osman Nuri
dc.contributor.authorKhalaf, Mohammed
dc.contributor.authorMohammed, Alaa Hamid
dc.date.accessioned2021-05-15T12:49:38Z
dc.date.available2021-05-15T12:49:38Z
dc.date.issued2020
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, Elektrik - Elektronik Mühendisliği Bölümüen_US
dc.description4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 -- 22 October 2020 through 24 October 2020 -- -- 165025
dc.description.abstractThis paper 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. © 2020 IEEE.en_US
dc.identifier.doi10.1109/ISMSIT50672.2020.9254994
dc.identifier.isbn9781728190907
dc.identifier.scopus2-s2.0-85097665218
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/ISMSIT50672.2020.9254994
dc.identifier.urihttps://hdl.handle.net/20.500.12939/1079
dc.indekslendigikaynakScopus
dc.institutionauthorUçan, Osman Nuri
dc.institutionauthorMohammed, Alaa Hamid
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 - Proceedings
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMachine-Learning Classifiersen_US
dc.subjectPerformance Evaluationen_US
dc.subjectSCD Date Setsen_US
dc.subjectSickle Cell Disorderen_US
dc.titleImplementation a various types of machine learning approaches for biomedical datasets based on sickle cell disorder
dc.typeConference Object

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