Prediction of intrapartum fetal hypoxia considering feature selection algorithms and machine learning models.

ABSTRACT:

Introduction

Cardiotocography (CTG) consists of two biophysical signals that are fetal heart rate (FHR) and uterine contraction (UC). In this research area, the computerized systems are usually utilized to provide more objective and repeatable results.

Materials and methods

Feature selection algorithms are of great importance regarding the computerized systems to not only reduce the dimension of feature set but also to reveal the most relevant features without losing too much information. In this paper, three filters and two wrappers feature selection methods and machine learning models, which are artificial neural network (ANN), k-nearest neighbor (kNN), decision tree (DT), and support vector machine (SVM), are evaluated on a high dimensional feature set obtai

SUBMITTER: Comert Z 

PROVIDER: S-EPMC6702252 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature