Evaluation of Support Vector Machines and Random Forest Classifiers in a Real-Time Fetal Monitoring System based on Cardiotocography Data

Abstract

In this paper, we compare methods for evaluating the fetal state prediction based on Cardiotocography (CTG) data. Antepartum Fetal Monitoring provides information that can be used to predict the state of the fetus during labor to indicate the risk of a fetal acidosis (low blood pH from low oxygen levels). The effectiveness of these predictions is evaluated in a real-time clinical decision support system and extracts other features that can provide further information regarding the fetal state. This research differs from previous work in that all three fetal states (normal, suspect and pathological) are considered. The paper discusses the importance of machine learning in providing assistance for the obstetricians in 'suspect' cases. Results show that both Support Vector Machines and Random Forests had over 96% accuracy when predicting fetal outcomes, with SVM performing slightly better for suspect cases.

Meeting Name

2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2017 (2017: Aug. 23-25, Manchester, UK)

Department(s)

Engineering Management and Systems Engineering

Research Center/Lab(s)

Intelligent Systems Center

Comments

This research has been partially funded by the Ozark Biomedical Initiative, in association with the Phelps County Regional Medical Center, Rolla, MO, USA.

Keywords and Phrases

Cardiotocogram; Machine Learning; Random Forests; Support Vector Machines

International Standard Book Number (ISBN)

978-146738988-4

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Aug 2017

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