DIAGNOSING ABNORMALITY OF FOETUS USING MACHINE LEARNING ALGORITHMS

Authors

  • Abhijith Ragav Information Technology, SRM IST, Chennai - 603203, Tamil Nadu, India Author
  • P.Surya Computer Science and Engineering, SRM IST, Chennai - 603203, Tamil Nadu, India Author
  • Shivabhinav S.G Computer Science and Engineering, SRM IST, Chennai - 603203, Tamil Nadu, India Author
  • P.Akilandeswari Assistant Professor,Computer Science and Engineering, SRM IST, Chennai - 603203, Tamil Nadu, India Author

DOI:

https://doi.org/10.61841/ctpvcd78

Keywords:

Fetal Heart Rate,, Cardiotocography, K Nearest neighbours, Support Vector, Machine, Radial Basis Function, Extreme Gradient Boosting

Abstract

The primary objective of the paper is to utilise machine learning algorithms for diag- nosing sufferance caused to the foetus by parameters such as Fetal Heart Rate (FHR) recordings (section). The significance of this work is to accurately predict the foetus condition at a much earlier stage since it is very important to analyse the issue at the right time to avoid complications. Concluding results from Cardiotocography (CTG), a test which is used widely for estimating fetal distress poses a major challenge. This work benefits the medical community, by detecting the fetal distress conditions using features derived from CTG results at a preliminary stage of 30-35 gestational weeks. The acute state can be classified by a few indications such as decrease in oxygen content due to reduction of haemoglobin count of fetal unit and is usually a complication of labour. The paper deals with the prior prediction of foetal conditions so as to provide early and effective treatment.

 

Downloads

Download data is not yet available.

References

1. Divya Sampath V.N.H.G , Corns S.,Long S., Evaluation of Support Vector Machines and Random Forest Classifiers in a Real-time Fetal Monitoring System Based on Car- diotocography Data978-1-4673-8988-4/17/$31.00 2017 IEEE

2. Knack D, R Kruse, ”Obtaining interpretable fuzzy classification rules from medical data”, Artificial Intelligence in Medicine, vol. 16-2, pp. 149-169, June 1999.

3. Kaggle, ”UCI Cardiotocography”, Retrieved from the following link https://www.kaggle.com/propanon/ucicardiotocography

Downloads

Published

31.10.2020

Issue

Vol. 24 No. 8 (2020): 24 (8) 2020

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

You are free to:

  1. Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
  2. Adapt — remix, transform, and build upon the material for any purpose, even commercially.
  3. The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms:

  1. Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  2. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Notices:

You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .

No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.

How to Cite

Ragav, A., P.Surya, S.G, S., & P.Akilandeswari. (2020). DIAGNOSING ABNORMALITY OF FOETUS USING MACHINE LEARNING ALGORITHMS. International Journal of Psychosocial Rehabilitation, 24(8), 12897-12905. https://doi.org/10.61841/ctpvcd78