نمایش مختصر رکورد

dc.contributor.authorHajimirzaie, Saiedeh Sadaten_US
dc.contributor.authorTehranian, Najmehen_US
dc.contributor.authorMousavi, Seyed Abbasen_US
dc.contributor.authorGolabpour, Aminen_US
dc.contributor.authorMirzaii, Mehdien_US
dc.contributor.authorKeramat, Afsanehen_US
dc.contributor.authorKhosravi, Ahmaden_US
dc.date.accessioned1400-08-25T00:09:46Zfa_IR
dc.date.accessioned2021-11-16T00:09:46Z
dc.date.available1400-08-25T00:09:46Zfa_IR
dc.date.available2021-11-16T00:09:46Z
dc.date.issued2021-11-01en_US
dc.date.issued1400-08-10fa_IR
dc.date.submitted2020-11-02en_US
dc.date.submitted1399-08-12fa_IR
dc.identifier.citationHajimirzaie, Saiedeh Sadat, Tehranian, Najmeh, Mousavi, Seyed Abbas, Golabpour, Amin, Mirzaii, Mehdi, Keramat, Afsaneh, Khosravi, Ahmad. (2021). Predicting the Relation between Biopsychosocial Factors and Type of Childbirth using the Decision Tree Method: A Cohort Study. Iranian Journal of Medical Sciences, 46(6), 437-443. doi: 10.30476/ijms.2021.88777.1951en_US
dc.identifier.issn0253-0716
dc.identifier.issn1735-3688
dc.identifier.urihttps://dx.doi.org/10.30476/ijms.2021.88777.1951
dc.identifier.urihttps://ijms.sums.ac.ir/article_47707.html
dc.identifier.urihttps://iranjournals.nlai.ir/handle/123456789/843143
dc.description.abstractBackground: With the growing rate of cesarean sections, rising morbidity and mortality thereafter is an important health issue. Predictive models can identify individuals with a higher probability of cesarean section, and help them make better decisions. This study aimed to investigate the biopsychosocial factors associated with the method of childbirth and designed a predictive model using the decision tree C4.5 algorithm. Methods: In this cohort study, the sample included 170 pregnant women in the third trimester of pregnancy referring to Shahroud Health Care Centers (Semnan, Iran), from 2018 to 2019. Blood samples were taken from mothers to measure the estrogen hormone at baseline. Birth information was recorded at the follow-up time per 30-42 days postpartum. Chi square, independent samples t test, and Mann-Whitney were used for comparisons between the two groups. Modeling was performed with the help of MATLAB software and C4.5 decision tree algorithm using input variables and target variable (childbirth method). The data were divided into training and testing datasets using the 70-30% method. In both stages, sensitivity, specificity, and accuracy were evaluated by the decision tree algorithm.Results: Previous method of childbirth, maternal body mass index at childbirth, maternal age, and estrogen were the most significant factors predicting the childbirth method. The decision tree model's sensitivity, specificity, and accuracy were 85.48%, 94.34%, and 89.57% in the training stage, and 82.35%, 83.87%, and 83.33% in the testing stage, respectively.Conclusion: The decision tree model was designed with high accuracy successfully predicted the method of childbirth. By recognizing the contributing factors, policymakers can take preventive action. It should be noted that this article was published in preprint form on the website of research square (https://www.researchsquare.com/article/rs-34770/v1).en_US
dc.format.extent391
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoen_US
dc.publisherShiraz University of Medical Sciencesen_US
dc.relation.ispartofIranian Journal of Medical Sciencesen_US
dc.relation.isversionofhttps://dx.doi.org/10.30476/ijms.2021.88777.1951
dc.subjectCesarean Sectionen_US
dc.subjectEstrogensen_US
dc.subjectBiological factorsen_US
dc.subjectSocioeconomic factorsen_US
dc.titlePredicting the Relation between Biopsychosocial Factors and Type of Childbirth using the Decision Tree Method: A Cohort Studyen_US
dc.typeTexten_US
dc.typeOriginal Article(s)en_US
dc.contributor.departmentStudent Research Committee, School of Nursing and Midwifery, Shahroud University of Medical Sciences, Shahroud, Iranen_US
dc.contributor.departmentDepartment of Reproductive Health and Midwifery, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iranen_US
dc.contributor.departmentCenter for Health Related Social and Behavioral Sciences Research, Shahroud University of Medical Sciences, Shahroud, Iranen_US
dc.contributor.departmentSchool of Allied Medical Sciences, Shahroud University of Medical Sciences, Shahroud, Iranen_US
dc.contributor.departmentDepartment of Basic Sciences, School of Medicine, Shahroud University of Medical Sciences, Shahroud, Iranen_US
dc.contributor.departmentCenter for Health Related Social and Behavioral Sciences Research, Shahroud University of Medical Sciences, Shahroud, Iranen_US
dc.contributor.departmentOphthalmic Epidemiology Research Center, Shahroud University of Medical Sciences, Shahroud, Iranen_US
dc.citation.volume46
dc.citation.issue6
dc.citation.spage437
dc.citation.epage443
nlai.contributor.orcid0000-0003-1763-0351
nlai.contributor.orcid0000-0003-0953-0705
nlai.contributor.orcid0000-0001-7649-4033
nlai.contributor.orcid0000-0002-2509-0300
nlai.contributor.orcid0000-0002-1106-3782


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