Prediction of intrapartum fetal hypoxia considering feature selection algorithms and machine learning models

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dc.authoridKocamaz, Adnan Fatih/0000-0002-7729-8322
dc.authoridSengur, Abdulkadir/0000-0003-1614-2639
dc.authoridBudak, Ümit/0000-0003-4082-383X
dc.authorwosidKocamaz, Adnan Fatih/C-2820-2014
dc.authorwosidCömert, Zafer/F-1940-2016
dc.authorwosidSengur, Abdulkadir/Q-8023-2019
dc.authorwosidBudak, Ümit/AAI-2500-2020
dc.contributor.authorComert, Zafer
dc.contributor.authorSengur, Abdulkadir
dc.contributor.authorBudak, Umit
dc.contributor.authorKocamaz, Adnan Fatih
dc.date.accessioned2024-08-04T20:46:56Z
dc.date.available2024-08-04T20:46:56Z
dc.date.issued2019
dc.departmentİnönü Üniversitesien_US
dc.description.abstractIntroduction Cardiotocography (CTG) consists of two biophysical signals that are fetal heart rate (FHR) and uterine contraction (UC). In this research area, the computerized systems are usually utilized to provide more objective and repeatable results. Materials and Methods Feature selection algorithms are of great importance regarding the computerized systems to not only reduce the dimension of feature set but also to reveal the most relevant features without losing too much information. In this paper, three filters and two wrappers feature selection methods and machine learning models, which are artificial neural network (ANN), k-nearest neighbor (kNN), decision tree (DT), and support vector machine (SVM), are evaluated on a high dimensional feature set obtained from an open-access CTU-UHB intrapartum CTG database. The signals are divided into two classes as normal and hypoxic considering umbilical artery pH value (pH < 7.20) measured after delivery. A comprehensive diagnostic feature set forming the features obtained from morphological, linear, nonlinear, time-frequency and image-based time-frequency domains is generated first. Then, combinations of the feature selection algorithms and machine learning models are evaluated to achieve the most effective features as well as high classification performance. Results The experimental results show that it is possible to achieve better classification performance using lower dimensional feature set that comprises of more related features, instead of the high-dimensional feature set. The most informative feature subset was generated by considering the frequency of selection of the features by feature selection algorithms. As a result, the most efficient results were produced by selected only 12 relevant features instead of a full feature set consisting of 30 diagnostic indices and SVM model. Sensitivity and specificity were achieved as 77.40% and 93.86%, respectively. Conclusion Consequently, the evaluation of multiple feature selection algorithms resulted in achieving the best results.en_US
dc.identifier.doi10.1007/s13755-019-0079-z
dc.identifier.issn2047-2501
dc.identifier.issue1en_US
dc.identifier.pmid31435480en_US
dc.identifier.scopus2-s2.0-85075208172en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1007/s13755-019-0079-z
dc.identifier.urihttps://hdl.handle.net/11616/99054
dc.identifier.volume7en_US
dc.identifier.wosWOS:000482780300001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofHealth Information Science and Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBiomedical signal processingen_US
dc.subjectFetal heart rateen_US
dc.subjectFeature selectionen_US
dc.subjectClassificationen_US
dc.subjectMachine learningen_US
dc.titlePrediction of intrapartum fetal hypoxia considering feature selection algorithms and machine learning modelsen_US
dc.typeArticleen_US

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