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Selected Works of Steven Corns
Article
Evaluation of Support Vector Machines and Random Forest Classifiers in a Real-Time Fetal Monitoring System based on Cardiotocography Data
Proceedings of the 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (2017, Manchester, UK)
  • Vinayaka Nagendra Harikishan Gude Divya Sampath
  • Steven Corns, Missouri University of Science and Technology
  • Suzanna Long, Missouri University of Science and Technology
Link
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
8-1-2017
Publication Date
01 Aug 2017
Disciplines
Citation Information
Vinayaka Nagendra Harikishan Gude Divya Sampath, Steven Corns and Suzanna Long. "Evaluation of Support Vector Machines and Random Forest Classifiers in a Real-Time Fetal Monitoring System based on Cardiotocography Data" Proceedings of the 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (2017, Manchester, UK) (2017)
Available at: http://works.bepress.com/steven-corns/16/