Manuscript Number : EBHDC039
Early Stage Prediction of Caesarean Vs Normal Vaginal Delivery Using Artificial Intelligence
Authors(5) :-Naveena B, Pavithra U, Shalini R, Sivaranjini S, Nishanthini R 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
Naveena B Early Prediction of Delivery, CTG (Cardio Toco Graphy) Publication Details
Published in : Volume 5 | Issue 5 | March-April 2020 Article Preview
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
Date of Publication : 2020-03-05
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 145-152
Manuscript Number : EBHDC039
Publisher : Technoscience Academy
Journal URL : https://ijsrst.com/EBHDC039
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