Student Research Committee, School of Nursing and Midwifery, Shahroud University of Medical Sciences, Shahroud, Iran.
Department of Reproductive Health and Midwifery, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
Iran J Med Sci. 2021 Nov;46(6):437-443. doi: 10.30476/IJMS.2021.88777.1951.
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.
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 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.
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.
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).
随着剖宫产率的不断上升，随之而来的发病率和死亡率是一个重要的健康问题。预测模型可以识别出剖宫产可能性较高的个体，并帮助他们做出更好的决策。本研究旨在探讨与分娩方式相关的生物心理社会因素，并使用决策树 C4.5 算法设计预测模型。
在这项队列研究中，样本包括 2018 年至 2019 年期间来自伊朗塞姆南沙阿roud 医疗中心的 170 名处于妊娠晚期的孕妇。在基线时从母亲身上抽取血液样本以测量雌激素激素。在产后 30-42 天的随访时间记录分娩信息。使用卡方检验、独立样本 t 检验和 Mann-Whitney 检验对两组进行比较。使用 MATLAB 软件和 C4.5 决策树算法，借助输入变量和目标变量（分娩方式）对模型进行建模。使用 70-30%的方法将数据分为训练数据集和测试数据集。在两个阶段，决策树算法都通过敏感性、特异性和准确性进行评估。
既往分娩方式、分娩时的母体体重指数、母亲年龄和雌激素是预测分娩方式的最重要因素。决策树模型在训练阶段的敏感性、特异性和准确性分别为 85.48%、94.34%和 89.57%，在测试阶段分别为 82.35%、83.87%和 83.33%。
该决策树模型具有较高的准确性，成功预测了分娩方式。通过识别促成因素，政策制定者可以采取预防措施。需要注意的是，本文已在研究广场（https://www.researchsquare.com/article/rs-34770/v1）网站上以预印本形式发表。
