Early Stage Prediction of Caesarean Vs Normal Vaginal Delivery Using Artificial Intelligence

Authors

  • Naveena B  Students, Department of biomedical Engineering, Dhanalakshmi Srinivasan Institute of Technology, Trichy, Tamilnadu, India
  • Pavithra U  Students, Department of biomedical Engineering, Dhanalakshmi Srinivasan Institute of Technology, Trichy, Tamilnadu, India
  • Shalini R  Students, Department of biomedical Engineering, Dhanalakshmi Srinivasan Institute of Technology, Trichy, Tamilnadu, India
  • Sivaranjini S  Students, Department of biomedical Engineering, Dhanalakshmi Srinivasan Institute of Technology, Trichy, Tamilnadu, India
  • Nishanthini R  Assistant professor in Department of Biomedical Engineering, Dhanalakshmi Srinivasan Institute of Technology, Trichy, Tamilnadu, India

Keywords:

Early Prediction of Delivery, CTG (Cardio Toco Graphy)

Abstract

Machine learning techniques provide learning mechanism that can be used to induce knowledge from data. A few studies exist on the use of machine learning techniques for medical diagnosis, prediction and treatment. In this study we evaluate different machine learning techniques for birth classification (cesarean or normal). Data on cesarean section is collected and different medical factors are identified that result in cesarean births. A birth classification model is built using decision tree and artificial neural networks. In this paper, we provide method of classifying caesarean section and normal vaginal deliveries using fetal heart rate signals and uterine contractions using Artificial intelligence. Here we predict the status of fetal using machine learning technique

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International Journal of Scientific Research in Science and Technology

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Published

2020-03-05

Issue

Volume 5, Issue 5, March-April-2020

Section

Research Articles

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How to Cite

[1]
Naveena B, Pavithra U, Shalini R, Sivaranjini S, Nishanthini R "Early Stage Prediction of Caesarean Vs Normal Vaginal Delivery Using Artificial Intelligence" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 5, Issue 5, pp.145-152, March-April-2020.