Predicting the Relation between Biopsychosocial Factors and Type of Childbirth using the Decision Tree Method: A Cohort Study

Background: 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 biopsycho...

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Autores principales:	Saiedeh Sadat Hajimirzaie, Najmeh Tehranian, Seyed Abbas Mousavi, Amin Golabpour, Mehdi Mirzaii, Afsaneh Keramat, Ahmad Khosravi
Formato:	article
Lenguaje:	EN
Publicado:	Shiraz University of Medical Sciences 2021
Materias:	cesarean section estrogens biological factors socioeconomic factors Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/f9ade92a638342b5b43002cdc73a9e6f
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spelling	oai:doaj.org-article:f9ade92a638342b5b43002cdc73a9e6f2021-11-09T06:23:38ZPredicting the Relation between Biopsychosocial Factors and Type of Childbirth using the Decision Tree Method: A Cohort Study0253-07161735-368810.30476/ijms.2021.88777.1951https://doaj.org/article/f9ade92a638342b5b43002cdc73a9e6f2021-11-01T00:00:00Zhttps://ijms.sums.ac.ir/article_47707_c152612ebeba5e6dc5fcae32c62006f7.pdfhttps://doaj.org/toc/0253-0716https://doaj.org/toc/1735-3688Background: 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).Saiedeh Sadat HajimirzaieNajmeh TehranianSeyed Abbas MousaviAmin GolabpourMehdi MirzaiiAfsaneh KeramatAhmad KhosraviShiraz University of Medical Sciencesarticlecesarean sectionestrogensbiological factorssocioeconomic factorsMedicine (General)R5-920ENIranian Journal of Medical Sciences, Vol 46, Iss 6, Pp 437-443 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	cesarean section estrogens biological factors socioeconomic factors Medicine (General) R5-920
spellingShingle	cesarean section estrogens biological factors socioeconomic factors Medicine (General) R5-920 Saiedeh Sadat Hajimirzaie Najmeh Tehranian Seyed Abbas Mousavi Amin Golabpour Mehdi Mirzaii Afsaneh Keramat Ahmad Khosravi Predicting the Relation between Biopsychosocial Factors and Type of Childbirth using the Decision Tree Method: A Cohort Study
description	Background: 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).
format	article
author	Saiedeh Sadat Hajimirzaie Najmeh Tehranian Seyed Abbas Mousavi Amin Golabpour Mehdi Mirzaii Afsaneh Keramat Ahmad Khosravi
author_facet	Saiedeh Sadat Hajimirzaie Najmeh Tehranian Seyed Abbas Mousavi Amin Golabpour Mehdi Mirzaii Afsaneh Keramat Ahmad Khosravi
author_sort	Saiedeh Sadat Hajimirzaie
title	Predicting the Relation between Biopsychosocial Factors and Type of Childbirth using the Decision Tree Method: A Cohort Study
title_short	Predicting the Relation between Biopsychosocial Factors and Type of Childbirth using the Decision Tree Method: A Cohort Study
title_full	Predicting the Relation between Biopsychosocial Factors and Type of Childbirth using the Decision Tree Method: A Cohort Study
title_fullStr	Predicting the Relation between Biopsychosocial Factors and Type of Childbirth using the Decision Tree Method: A Cohort Study
title_full_unstemmed	Predicting the Relation between Biopsychosocial Factors and Type of Childbirth using the Decision Tree Method: A Cohort Study
title_sort	predicting the relation between biopsychosocial factors and type of childbirth using the decision tree method: a cohort study
publisher	Shiraz University of Medical Sciences
publishDate	2021
url	https://doaj.org/article/f9ade92a638342b5b43002cdc73a9e6f
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Predicting the Relation between Biopsychosocial Factors and Type of Childbirth using the Decision Tree Method: A Cohort Study

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