Osong Public Health Res Perspect.  2017 Jun;8(3):195-200. 10.24171/j.phrp.2017.8.3.06.

Prevalence and Determinants of Preterm Birth in Tehran, Iran: A Comparison between Logistic Regression and Decision Tree Methods

• Amini P
• Maroufizadeh S
• Samani RO
• Hamidi O
• Sepidarkish M

• KMID: 2394474
• DOI: http://doi.org/10.24171/j.phrp.2017.8.3.06

Abstract

OBJECTIVES
Preterm birth (PTB) is a leading cause of neonatal death and the second biggest cause of death in children under five years of age. The objective of this study was to determine the prevalence of PTB and its associated factors using logistic regression and decision tree classification methods.
METHODS
This cross-sectional study was conducted on 4,415 pregnant women in Tehran, Iran, from July 6-21, 2015. Data were collected by a researcher-developed questionnaire through interviews with mothers and review of their medical records. To evaluate the accuracy of the logistic regression and decision tree methods, several indices such as sensitivity, specificity, and the area under the curve were used.
RESULTS
The PTB rate was 5.5% in this study. The logistic regression outperformed the decision tree for the classification of PTB based on risk factors. Logistic regression showed that multiple pregnancies, mothers with preeclampsia, and those who conceived with assisted reproductive technology had an increased risk for PTB (p < 0.05).
CONCLUSION
Identifying and training mothers at risk as well as improving prenatal care may reduce the PTB rate. We also recommend that statisticians utilize the logistic regression model for the classification of risk groups for PTB.

Keyword

preterm birth; risk factor; infant; logistic regression; decision tree

MeSH Terms

Cause of Death
Child
Classification
Cross-Sectional Studies
Decision Trees*
Female
Humans
Infant
Iran*
Logistic Models*
Medical Records
Methods*
Mothers
Perinatal Death
Pre-Eclampsia
Pregnancy
Pregnancy, Multiple
Pregnant Women
Premature Birth*
Prenatal Care
Prevalence*
Reproductive Techniques, Assisted
Risk Factors
Sensitivity and Specificity