Determination of fetal state from cardiotocogram using LS-SVM with particle swarm optimization and binary decision tree
dc.contributor.buuauthor | Yılmaz, Ersen | |
dc.contributor.buuauthor | Kılıkçıer, Çaǧlar | |
dc.contributor.department | Uludağ Üniversitesi/Mühendislik Fakültesi/Elektrik ve Elektronik Mühendisliği Bölümü. | tr_TR |
dc.contributor.orcid | 0000-0001-7933-1643 | tr_TR |
dc.contributor.researcherid | G-3554-2013 | tr_TR |
dc.contributor.researcherid | AAH-3031-2021 | tr_TR |
dc.contributor.scopusid | 56965095300 | tr_TR |
dc.contributor.scopusid | 55946623600 | tr_TR |
dc.date.accessioned | 2022-06-21T11:33:28Z | |
dc.date.available | 2022-06-21T11:33:28Z | |
dc.date.issued | 2013 | |
dc.description.abstract | We use least squares support vector machine (LS-SVM) utilizing a binary decision tree for classification of cardiotocogram to determine the fetal state. The parameters of LS-SVM are optimized by particle swarm optimization. The robustness of the method is examined by running 10-fold cross-validation. The performance of the method is evaluated in terms of overall classification accuracy. Additionally, receiver operation characteristic analysis and cobweb representation are presented in order to analyze and visualize the performance of the method. Experimental results demonstrate that the proposed method achieves a remarkable classification accuracy rate of 91.62%. | en_US |
dc.identifier.citation | Yilmaz, E. ve Kılıkçıer, Ç. (2013). "Determination of fetal state from cardiotocogram using LS-SVM with particle swarm optimization and binary decision tree". Computational and Mathematical Methods in Medicine, 2013. | en_US |
dc.identifier.issn | 1748-670X | |
dc.identifier.issn | 1748-6718 | |
dc.identifier.pubmed | 24288574 | tr_TR |
dc.identifier.scopus | 2-s2.0-84888869975 | tr_TR |
dc.identifier.uri | https://doi.org/10.1155/2013/487179 | |
dc.identifier.uri | https://www.hindawi.com/journals/cmmm/2013/487179/ | |
dc.identifier.uri | http://hdl.handle.net/11452/27335 | |
dc.identifier.volume | 2013 | tr_TR |
dc.identifier.wos | 000326751100001 | tr_TR |
dc.indexed.pubmed | PubMed | en_US |
dc.indexed.scopus | Scopus | en_US |
dc.indexed.wos | SCIE | en_US |
dc.language.iso | en | en_US |
dc.publisher | Hindawi | en_US |
dc.relation.journal | Computational and Mathematical Methods in Medicine | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi | tr_TR |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Mathematical & computational biology | en_US |
dc.subject | Heart-rate | en_US |
dc.subject | Classification | en_US |
dc.subject | Performance | en_US |
dc.subject | System | en_US |
dc.subject | Risk | en_US |
dc.subject | Binary trees | en_US |
dc.subject | Decision trees | en_US |
dc.subject | Particle swarm optimization (PSO) | en_US |
dc.subject | 10-fold cross-validation | en_US |
dc.subject | Binary decision trees | en_US |
dc.subject | Cardiotocogram | en_US |
dc.subject | Classification accuracy | en_US |
dc.subject | Least squares support vector machines | en_US |
dc.subject | Operation characteristic | en_US |
dc.subject | Support vector machines | en_US |
dc.subject.emtree | Article | en_US |
dc.subject.emtree | Cardiotocograph | en_US |
dc.subject.emtree | Cardiotocography | en_US |
dc.subject.emtree | Classification algorithm | en_US |
dc.subject.emtree | Clinical evaluation | en_US |
dc.subject.emtree | Decision tree | en_US |
dc.subject.emtree | Diagnostic accuracy | en_US |
dc.subject.emtree | Fetus | en_US |
dc.subject.emtree | Fetus development | en_US |
dc.subject.emtree | Human | en_US |
dc.subject.emtree | Image analysis | en_US |
dc.subject.emtree | Intelligence | en_US |
dc.subject.emtree | Learning algorithm | en_US |
dc.subject.emtree | Least square support vector machine | en_US |
dc.subject.emtree | Machine learning | en_US |
dc.subject.emtree | Nonhuman | en_US |
dc.subject.emtree | Parameters | en_US |
dc.subject.emtree | Particle swarm optimization | en_US |
dc.subject.emtree | Process optimization | en_US |
dc.subject.emtree | Receiver operating characteristic | en_US |
dc.subject.emtree | Support vector machine | en_US |
dc.subject.emtree | Artificial intelligence | en_US |
dc.subject.emtree | Cardiotocography | en_US |
dc.subject.emtree | Decision support system | en_US |
dc.subject.emtree | Decision tree | ten_US |
dc.subject.emtree | Evaluation study | en_US |
dc.subject.emtree | Female | en_US |
dc.subject.emtree | Pregnancy | en_US |
dc.subject.emtree | Regression analysis | en_US |
dc.subject.emtree | Statistics and numerical data | en_US |
dc.subject.emtree | Support vector machine | en_US |
dc.subject.emtree | Validation study | en_US |
dc.subject.emtree | Statistics | en_US |
dc.subject.mesh | Artificial intelligence | en_US |
dc.subject.mesh | Cardiotocography | en_US |
dc.subject.mesh | Decision support systems, clinical | en_US |
dc.subject.mesh | Decision trees | en_US |
dc.subject.mesh | Female | en_US |
dc.subject.mesh | Humans | en_US |
dc.subject.mesh | Least-squares analysis | en_US |
dc.subject.mesh | Pregnancy | en_US |
dc.subject.mesh | ROC curve | en_US |
dc.subject.mesh | Support vector machines | en_US |
dc.subject.scopus | Cardiotocography; Fetal Heart Rate; Pregnancy | en_US |
dc.subject.wos | Mathematical & Computational Biology | en_US |
dc.title | Determination of fetal state from cardiotocogram using LS-SVM with particle swarm optimization and binary decision tree | en_US |
dc.type | Article | |
dc.wos.quartile | Q3 | en_US |