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Call for Papers:Vol.12 Issue.2

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Title: :  Fetal Health Classification based on CTG using Machine learning
PaperId: :  20212
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 9 Issue 3 2023
DUI:    16.0415/IJARIIE-20212
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Radhika Vinod AgarwalBangalore Institute of technology
Akansha KediaBangalore Institute of technology
Yusra Naheed Bangalore Institute of technology
Sanjitha NBangalore Institute of technology
Manasa T PBangalore Institute of technology

Abstract

Computer science and engineering
Fetal health, Cardiotocography, Classification
The UN has estimated that 24 million babies were born in India in 2017 and 35,000 mothers died during or shortly after birth, with MMR at 145 per 100,000 live births, or 12% of parental deaths worldwide. Classification of fetal health is an important aspect of child care and can help prevent negative consequences. Cardiotocography (CTG) is a method often used to monitor fetal health, but its interpretation can be subjective and inaccurate. In recent years, machine learning methodologies and techniques have been proposed as a solution for improvement in the accuracy of CTG-based classification of fetus. In this study, we developed a classifier that automatically predicts fetal health using different learning machines. We used a database of CTG data from the University of California, Irvine's Machine Learning Repository containing 2126 subjects and 21 features, including 1655 healthy subjects, 295 unhealthy subjects and pathologically diseased there are 176 subjects. The proposed model has the potential to improve the accuracy and purpose of CTG-based fetal health classification, leading to better prenatal care and better outcomes for mothers and foetuses. As a comparison, the best model for prediction is random forest with 96% accuracy.

Citations

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IJARIIE Radhika Vinod Agarwal, Akansha Kedia, Yusra Naheed , Sanjitha N, and Manasa T P. "Fetal Health Classification based on CTG using Machine learning" International Journal Of Advance Research And Innovative Ideas In Education Volume 9 Issue 3 2023 Page 1177-1182
MLA Radhika Vinod Agarwal, Akansha Kedia, Yusra Naheed , Sanjitha N, and Manasa T P. "Fetal Health Classification based on CTG using Machine learning." International Journal Of Advance Research And Innovative Ideas In Education 9.3(2023) : 1177-1182.
APA Radhika Vinod Agarwal, Akansha Kedia, Yusra Naheed , Sanjitha N, & Manasa T P. (2023). Fetal Health Classification based on CTG using Machine learning. International Journal Of Advance Research And Innovative Ideas In Education, 9(3), 1177-1182.
Chicago Radhika Vinod Agarwal, Akansha Kedia, Yusra Naheed , Sanjitha N, and Manasa T P. "Fetal Health Classification based on CTG using Machine learning." International Journal Of Advance Research And Innovative Ideas In Education 9, no. 3 (2023) : 1177-1182.
Oxford Radhika Vinod Agarwal, Akansha Kedia, Yusra Naheed , Sanjitha N, and Manasa T P. 'Fetal Health Classification based on CTG using Machine learning', International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 3, 2023, p. 1177-1182. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Fetal_Health_Classification_based_on_CTG_using_Machine_learning_ijariie20212.pdf (Accessed : 16 March 2026).
Harvard Radhika Vinod Agarwal, Akansha Kedia, Yusra Naheed , Sanjitha N, and Manasa T P. (2023) 'Fetal Health Classification based on CTG using Machine learning', International Journal Of Advance Research And Innovative Ideas In Education, 9(3), pp. 1177-1182IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Fetal_Health_Classification_based_on_CTG_using_Machine_learning_ijariie20212.pdf (Accessed : 16 March 2026)
IEEE Radhika Vinod Agarwal, Akansha Kedia, Yusra Naheed , Sanjitha N, and Manasa T P, "Fetal Health Classification based on CTG using Machine learning," International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 3, pp. 1177-1182, May-Jun 2023. [Online]. Available: https://ijariie.com/AdminUploadPdf/Fetal_Health_Classification_based_on_CTG_using_Machine_learning_ijariie20212.pdf [Accessed : 16 March 2026].
Turabian Radhika Vinod Agarwal, Akansha Kedia, Yusra Naheed , Sanjitha N, and Manasa T P. "Fetal Health Classification based on CTG using Machine learning." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 9 number 3 (16 March 2026).
Vancouver Radhika Vinod Agarwal, Akansha Kedia, Yusra Naheed , Sanjitha N, and Manasa T P. Fetal Health Classification based on CTG using Machine learning. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2023 [Cited : 16 March 2026]; 9(3) : 1177-1182. Available from: https://ijariie.com/AdminUploadPdf/Fetal_Health_Classification_based_on_CTG_using_Machine_learning_ijariie20212.pdf
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