Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 9, 13 November 2023
Open
Access | Article
Fetal health screening model and element analysis
* Author to whom correspondence should be addressed.
Abstract
The United Nations’ Sustainable Development Goals has mentioned to reduce child mortality. That is also a crucial indicator of human progress. The UN hopes that all countries will eradicate preventable deaths of newborns at the end of 2030. Cardiotocogram (CTG) can be used to identify in-danger women during pregnancy. The aim of this article is to apply machine learning algorithm techniques on CTG data to ensure fetal well-being. CTG data of 2126 samples and 22 variables were obtained from the CTG exams on Kaggle. Two different classification models were trained through the data. In order to predict ‘Normal’, ‘Suspect’, and ‘Pathological’ fetal states, each class had its own sensitivity, precision and F1 score. Each model has its overall accuracy. Determined by obstetricians’ interpretation of CTG, ‘Normal’ state accounted for 57%, ‘Suspect’ state accounted for 23% and ‘Pathological’ state accounted for 20%. The classification models generated by Logistic Regression and Random Forest to predict the suspect and pathological state of the fetus by tracing CTG. They had high precision of 86% and 94% respectively. However, the classification model developed by Random Forest had higher prediction accuracy for a negative fetal outcome. Healthcare workers without professional training in low-income countries have the opportunity to utilize this model for the purpose of prioritizing pregnant women in hard-to-reach regions, ensuring they receive timely referrals and appropriate follow-up care.
Keywords
Fetal Health, Logistic Regression, Random Forest
References
1. Hoodbhoy, Z., Noman, M., Shafique, A., Nasim, A., Chowdhury, D., & Hasan, B. Use of machine learning algorithms for prediction of fetal risk using cardiotocographic data. International Journal of Applied and Basic Medical Research,2019, 9(4), 226-230. doi:https://doi.org/ 10.4103/ijabmr.IJABMR_370_18
2. Liu L, Oza S, Hogan D, Chu Y, Perin J, Zhu J, et al. Global, regional, and national causes of under‑5 mortality in 2000‑15: An updated systematic analysis with implications for the sustainable development goals. Lancet 2016;388:3027‑35.
3. Subasi, A., Kadasa, B., & Kremic, E. (2020). Classification of the Cardiotocogram Data for Anticipation of Fetal Risks using Bagging Ensemble Classifier. Procedia Computer Science, 168, 34–39. https://doi.org/10.1016/j.procs.2020.02.248
4. R. M. Grivell, Z. A. Gillian, M. L. Gyte, and D. Devane, “Antenatal cardiotocography for fetal assessment,” in Cochrane Database of Systematic Reviews, no. 9pp. 1–57, John Wiley &Sons, Ltd, 2015.
5. National Institute for Health and Clinical Excellence, Diabetes in pregnancy: management of diabetes and its complications from preconception to the postnatal period, London, 2008.
6. Alam, M. T., Khan, M. A. I., Dola, N. N., Tazin, T., Khan, M. M., Albraikan, A. A., & Almalki, F. A. Comparative Analysis of Different Efficient Machine Learning Methods for Fetal Health Classification. Applied Bionics and Biomechanics, 2022, 1–12. https://doi.org/10.1155/2022/6321884
7. Jebadurai, I. J., Paulraj, G. J. L., Jebadurai, J., & Silas, S.Experimental analysis of filtering-based feature selection techniques for fetal health classification. Serbian Journal of Electrical Engineering,2022, 19(2), 207-224.
8. Borboudakis, G., & Tsamardinos, I. (2019). Forward-backward selection with early dropping. The Journal of Machine Learning Research, 20(1), 276-314.
9. James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023, July 1). An Introduction to Statistical Learning: With Applications in Python. Springer.
10. Asha, J., & Meenakowshalya, A. Fake news detection using n-gram analysis and machine learning algorithms. Journal of Mobile Computing, Communications & Mobile Networks,2021, 8(1), 33-43p.
11. James Bergstra and Yoshua Bengio. 2012. Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 1 (January 2012), 281–305
12. Wang, L., Han, M., Li, X., Zhang, N., & Cheng, H. (2021). Review of classification methods on unbalanced data sets. IEEE Access, 9, 64606-64628.
13. Levent Serinol.(2021).“Fetal Health Data Profile, Boruta & Model Stacking” Retrieve on 28 July 2023.Retrieved from: https://www.kaggle.com/code/landfallmotto/fetal-health-data-profile-boruta- model-stacking.
14. Paria Agharabi. “Step by Step Fetal Health Prediction-99%-Detailed” Kaggle.com, 2020, Retrieved on 31 July 2023. Retrieved from: https://www.kaggle.com/code/pariaagharabi/step-by-step-fetal-health-prediction99detailed/notebook.
15. Karnika Kapoor. “Fetal Health Classification” Kaggle.com, 2021. Retrieved on 29 July 2023.Retrieved from:https://www.kaggle.com/code/ karnikakapoor/fetal-health-classification.
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
- ISBN (Print)
- 978-1-83558-129-2
- ISBN (Online)
- 978-1-83558-130-8
- Published Date
- 13 November 2023
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/9/20240727
- Copyright
- 13 November 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited