初产妇剖宫产的决定因素：分类方法比较
Determinants of Cesarean Section among Primiparas: A Comparison of Classification Methods.
作者信息
Maroufizadeh Saman, Amini Payam, Hosseini Mostafa, Almasi-Hashiani Amir, Mohammadi Maryam, Navid Behnaz, Omani-Samani Reza
机构信息
Department of Epidemiology and Reproductive Health, Reproductive Epidemiology Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran.
Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
出版信息
Iran J Public Health. 2018 Dec;47(12):1913-1922.
BACKGROUND
Over the last few decades, Cesarean section (CS) rates have increased significantly worldwide particularly in Iran. Classification methods including logistic regression (LR), random forest (RF) and artificial neural network (ANN) were used to identify factors related to CS among primipars.
METHODS
This cross-sectional study included 2120 primipars who gave singleton birth in Tehran, Iran between 6 and 21 July 2015. To identify factor associated with CS, the classification methods were compared in terms of sensitivity, specificity, and accuracy.
RESULTS
The CS rate was 72.1%. Mother's age, SES, BMI, baby's head circumference and infant weight were the most important determinant variables for CS as identified by the ANN method which had the highest accuracy (0.70). The association of RF predictions and observed values was 0.36 (kappa).
CONCLUSION
The ANN method had the best performance that classified CS delivery compared to the RF and LR methods. The ANN method might be used as an appropriate method for such data.
背景
在过去几十年间，全球剖宫产（CS）率显著上升，尤其是在伊朗。包括逻辑回归（LR）、随机森林（RF）和人工神经网络（ANN）在内的分类方法被用于识别初产妇中与剖宫产相关的因素。
方法
这项横断面研究纳入了2015年7月6日至21日期间在伊朗德黑兰单胎分娩的2120名初产妇。为了识别与剖宫产相关的因素，对这些分类方法的敏感性、特异性和准确性进行了比较。
结果
剖宫产率为72.1%。母亲年龄、社会经济地位、体重指数、婴儿头围和婴儿体重是人工神经网络方法确定的剖宫产最重要的决定变量，该方法具有最高的准确率（0.70）。随机森林预测与观察值的关联度为0.36（kappa值）。
结论
与随机森林和逻辑回归方法相比，人工神经网络方法在分类剖宫产分娩方面表现最佳。人工神经网络方法可能是处理此类数据的合适方法。
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