J Korean Med Sci.  2019 Apr;34(16):e128. 10.3346/jkms.2019.34.e128.

Artificial Neural Network Analysis of Spontaneous Preterm Labor and Birth and Its Major Determinants

• Lee KS
• Ahn KH

• KMID: 2443593
• DOI: http://doi.org/10.3346/jkms.2019.34.e128

Abstract

BACKGROUND
Little research based on the artificial neural network (ANN) is done on preterm birth (spontaneous preterm labor and birth) and its major determinants. This study uses an ANN for analyzing preterm birth and its major determinants.
METHODS
Data came from Anam Hospital in Seoul, Korea, with 596 obstetric patients during March 27, 2014 - August 21, 2018. Six machine learning methods were applied and compared for the prediction of preterm birth. Variable importance, the effect of a variable on model performance, was used for identifying major determinants of preterm birth. Analysis was done in December, 2018.
RESULTS
The accuracy of the ANN (0.9115) was similar with those of logistic regression and the random forest (0.9180 and 0.8918, respectively). Based on variable importance from the ANN, major determinants of preterm birth are body mass index (0.0164), hypertension (0.0131) and diabetes mellitus (0.0099) as well as prior cone biopsy (0.0099), prior placenta previa (0.0099), parity (0.0033), cervical length (0.0001), age (0.0001), prior preterm birth (0.0001) and myomas & adenomyosis (0.0001).
CONCLUSION
For preventing preterm birth, preventive measures for hypertension and diabetes mellitus are required alongside the promotion of cervical-length screening with different guidelines across the scope/type of prior conization.

Keyword

Preterm Birth; Hypertension; Diabetes Mellitus; Prior Conization; Cervical-Length Screening

MeSH Terms

Adenomyosis
Biopsy
Body Mass Index
Conization
Diabetes Mellitus
Female
Forests
Humans
Hypertension
Korea
Logistic Models
Machine Learning
Mass Screening
Myoma
Obstetric Labor, Premature*
Parity
Parturition*
Placenta Previa
Pregnancy
Premature Birth
Seoul