Implementation a various types of machine learning approaches for biomedical datasets based on sickle cell disorder
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Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This 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.
Açıklama
Anahtar Kelimeler
Machine-Learning Classifiers, Sickle Cell Disorder, SCD Date Sets, Performance Evaluation
Kaynak
WoS Q Değeri
Scopus Q Değeri
Cilt
Sayı
Künye
Dheyab, 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.
