Comparison of Machine Learning Techniques for Fetal Heart Rate Classification

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dc.authoridKocamaz, Adnan Fatih/0000-0002-7729-8322
dc.authoridComert, Zafer/0000-0001-5256-7648
dc.authorwosidKocamaz, Adnan Fatih/C-2820-2014
dc.authorwosidCömert, Zekeriya Yaşar/AAZ-4666-2020
dc.authorwosidComert, Zafer/F-1940-2016
dc.contributor.authorComert, Z.
dc.contributor.authorKocamaz, A. F.
dc.date.accessioned2024-08-04T20:44:06Z
dc.date.available2024-08-04T20:44:06Z
dc.date.issued2017
dc.departmentİnönü Üniversitesien_US
dc.description3rd International Conference on Computational and Experimental Science and Engineering (ICCESEN) -- OCT 19-24, 2016 -- Antalya, TURKEYen_US
dc.description.abstractCardiotocography is a monitoring technique providing important and vital information on fetal status during antepartum and intrapartum periods. The advances in modern obstetric practice allowed many robust and reliable machine learning techniques to be utilized in classifying fetal heart rate signals. The role of machine learning approaches in diagnosing diseases is becoming increasingly essential and intertwined. The main aim of the present study is to determine the most efficient machine learning technique to classify fetal heart rate signals. Therefore, the research has been focused on the widely used and practical machine learning techniques, such as artificial neural network, support vector machine, extreme learning machine, radial basis function network, and random forest. In a comparative way, fetal heart rate signals were classified as normal or hypoxic using the aforementioned machine learning techniques. The performance metrics derived from confusion matrix were used to measure classifiers' success. According to experimental results, although all machine learning techniques produced satisfactory results, artificial neural network yielded the rather well results with the sensitivity of 99.73% and specificity of 97.94%. The study results show that the artificial neural network was superior to other algorithms.en_US
dc.identifier.doi10.12693/APhysPolA.132.451
dc.identifier.endpage454en_US
dc.identifier.issn0587-4246
dc.identifier.issn1898-794X
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85033411391en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage451en_US
dc.identifier.urihttps://doi.org/10.12693/APhysPolA.132.451
dc.identifier.urihttps://hdl.handle.net/11616/98020
dc.identifier.volume132en_US
dc.identifier.wosWOS:000412881200013en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPolish Acad Sciences Inst Physicsen_US
dc.relation.ispartofActa Physica Polonica Aen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectcardiotocographyen_US
dc.subjectmachine learning techniquesen_US
dc.subjectclassificationen_US
dc.titleComparison of Machine Learning Techniques for Fetal Heart Rate Classificationen_US
dc.typeConference Objecten_US

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