Fetal health classification from cardiotocographic data using machine learning-Reference-Cited by-同舟云学术

Fetal health classification from cardiotocographic data using machine learning

Published:2021-12 Issue: Volume: Page:
ISSN:0266-4720
Container-title:Expert Systems
language:en
Short-container-title:Expert Systems

Author:

Mehbodniya Abolfazl¹,Lazar Arokia Jesu Prabhu²,Webber Julian³,Sharma Dilip Kumar⁴,Jayagopalan Santhosh⁵,K Kousalya⁶,Singh Pallavi⁷,Rajan Regin⁸,Pandya Sharnil⁹,Sengan Sudhakar¹⁰^ORCID

Affiliation:

1. Department of Electronics and Communications Engineering Kuwait College of Science and Technology Kuwait Kuwait

2. Department of Computer Science and Engineering CMR Institute of Technology Hyderabad India

3. Osaka University Osaka Japan

4. Department of Mathematics Jaypee University of Engineering and Technology Guna Madhya Pradesh India

5. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India

6. Department of Computer Science and Engineering Kongu Engineering College Perundurai Tamil Nadu India

7. School of Allied Health Sciences Jaipur National University Jaipur Rajasthan India

8. Department of Computer Science and Engineering Adhiyamaan College of Engineering Hosur Tamil Nadu India

9. Department of CSIT and AIML Symbiosis International University Pune Maharashtra India

10. Department of Computer Science and Engineering PSN College of Engineering and Technology Tirunelveli Tamil Nadu India

Publisher

Wiley

Subject

Artificial Intelligence,Computational Theory and Mathematics,Theoretical Computer Science,Control and Systems Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/exsy.12899

Reference29 articles.

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2. Classification of cardiotocograms using random forest classifier and selection of important features from cardiotocogram signal

3. Sisporto 2.0: A program for automated analysis of cardiotocograms

4. Neuro‐fuzzy feature selection approach based on linguistic hedges for medical diagnosis;Azar A. T.;International Journal of Modelling, Identification and Control,2014

5. Prediction of intrapartum fetal hypoxia considering feature selection algorithms and machine learning models;Cömert Z.;Health Information Science and Systems,2019

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