Machine Learning-Based Prediction Model of Preterm Birth Using Electronic Health Record.

ABSTRACT:

Objective

Preterm birth (PTB) was one of the leading causes of neonatal death. Predicting PTB in the first trimester and second trimester will help improve pregnancy outcomes. The aim of this study is to propose a prediction model based on machine learning algorithms for PTB.

Method

Data for this study were reviewed from 2008 to 2018, and all the participants included were selected from a hospital in China. Six algorisms, including Naive Bayesian (NBM), support vector machine (SVM), random forest tree (RF), artificial neural networks (ANN), K-means, and logistic regression, were used to predict PTB. The receiver operating characteristic curve (ROC), accuracy, sensitivity, and specificity were used to assess the performance of the model.

Results

A total of 9550 pregnan

SUBMITTER: Sun Q 

PROVIDER: S-EPMC9020923 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature