Title of article
Determinants of Cesarean Section among Primiparas: A Comparison of Classification Methods
Author/Authors
MAROUFIZADEH, Saman Department of Epidemiology and Reproductive Health - Reproductive Epidemiology Research Center - Royan Institute for Reproduc-tive Biomedicine, ACECR, Tehran, Iran , AMINI, Payam Department of Epidemiology and Reproductive Health - Reproductive Epidemiology Research Center - Royan Institute for Reproduc-tive Biomedicine, ACECR, Tehran, Iran , HOSSEINI, Mostafa Department of Epidemiology and Biostatistics - School of Public Health - Tehran University of Medical Sciences, Tehran, Iran , ALMASI-HASHIANI, Amir Department of Epidemiology and Reproductive Health - Reproductive Epidemiology Research Center - Royan Institute for Reproduc-tive Biomedicine, ACECR, Tehran, Iran , MOHAMMADI, Maryam Department of Epidemiology and Reproductive Health - Reproductive Epidemiology Research Center - Royan Institute for Reproduc-tive Biomedicine, ACECR, Tehran, Iran , NAVID, Behnaz Department of Epidemiology and Reproductive Health - Reproductive Epidemiology Research Center - Royan Institute for Reproduc-tive Biomedicine, ACECR, Tehran, Iran , OMANI-SAMANI, Reza Department of Epidemiology and Reproductive Health - Reproductive Epidemiology Research Center - Royan Institute for Reproduc-tive Biomedicine, ACECR, Tehran, Iran
Pages
10
From page
1913
To page
1922
Abstract
Background: Over the last few decades, Cesarean section (CS) rates have increased significantly worldwide par-ticularly 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.
Keywords
Cesarean section , Primiparas , Artificial neural network , Random forest , Logistic regression , Classifi-cation
Journal title
Astroparticle Physics
Serial Year
2018
Record number
2426285
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