THE ASSESSMENT OF LH SURGE FOR PREDICTING OVULATION TIME USING CLINICAL, HORMONAL, AND ULTRASONIC INDEXES IN INFERTILE WOMEN WITH AN ENSEMBLE OF NEURAL NETWORKS

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dc.authorwosidGurgen, Fikret/AAD-6623-2020
dc.contributor.authorGURGEN, FS
dc.contributor.authorSIHMANOGLU, M
dc.contributor.authorVAROL, FG
dc.date.accessioned2024-06-12T10:52:38Z
dc.date.available2024-06-12T10:52:38Z
dc.date.issued1995
dc.departmentTrakya Üniversitesien_US
dc.description.abstractAn ensemble of independently trained neural networks (NN) is proposed for the assessment of luteinizing hormone (LH) surge for predicting ovulation time in infertile but ovulating women. The proposed ensemble involves a number of parallel NN modules. Each pair of the NNs learn specific data that are previously collected for monitoring timing function of LH levels. Training data which correspond to values of serum progesterone (ng ml(-1)), serum est radiol (pg ml(-1)), and follicle diameter (mm) are used to train NN pairs to approximate the function of the LH values. A reasonable and accurate estimation places ovulation approximately 10-12 h after the LH peak. The double-valued (bi-phasic) regions of training data are separated into two single-valued (bi-phasic) regions of training data are separated into two single-valued parts (not exactly preovulatory, postovulatory division) that can be learned by each module of the NN pair. During testing, after the initial decision to have single-valued sides, the assessment is obtained by a linear opinion pool (consensus rule) using the decisions of NNs on the corresponding side without waiting. The network ensemble has various desirable properties: high assessment accuracy of a double-valued multisource data, minimized learning and recall times, and a parallel structure. The ovulation time can be predicted through the assessment of LH peak with a better precision and fewer number of tests.en_US
dc.identifier.doi10.1016/0010-4825(95)00022-V
dc.identifier.endpage413en_US
dc.identifier.issn0010-4825
dc.identifier.issue4en_US
dc.identifier.pmid7497702en_US
dc.identifier.scopus2-s2.0-0029123709en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage405en_US
dc.identifier.urihttps://doi.org/10.1016/0010-4825(95)00022-V
dc.identifier.urihttps://hdl.handle.net/20.500.14551/18783
dc.identifier.volume25en_US
dc.identifier.wosWOS:A1995RU19100004en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers In Biology And Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectASSESSMENT OF LH SURGEen_US
dc.subjectPREDICTION OF OVULATION TIMEen_US
dc.subjectCLINICAL, HORMONAL AND ULTRASONIC INDEXESen_US
dc.subjectENSEMBLE OF NEURAL NETWORKSen_US
dc.subjectCONSENSUS OF MULTISOURCE DATAen_US
dc.subjectClassificationen_US
dc.titleTHE ASSESSMENT OF LH SURGE FOR PREDICTING OVULATION TIME USING CLINICAL, HORMONAL, AND ULTRASONIC INDEXES IN INFERTILE WOMEN WITH AN ENSEMBLE OF NEURAL NETWORKSen_US
dc.typeArticleen_US

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