Data Analysis and Optimal Prediction Model Search based on CTG Data

Siyuan Jiang; Tengyang Lian; Yuwen Wei

doi:10.54097/hset.v54i.9789

Authors

Siyuan Jiang
Tengyang Lian
Yuwen Wei

DOI:

https://doi.org/10.54097/hset.v54i.9789

Keywords:

CTG, fetal health, elastic net regression, Lasso model.

Abstract

This paper presents a data analysis and optimal prediction model search of fetal health based on cardiotocography (CTG) data. The objective of this study is to develop an accurate and efficient method to predict fetal health outcomes using CTG data. This paper first analyzes the dataset to identify potential predictors of fetal health and investigate their relationship with fetal distress. The result found a strong relationship between some of the variables in the dataset and fetal health. This paper then uses machine learning algorithms to build and compare several prediction models, including elastic net regression and lasso model. The result of this paper shows that a random forest model performs best in terms of AUC. The model can accurately predict fetal health outcomes with an AUC of 0.925. The finding of this paper suggests that CTG data analysis combined with machine learning algorithms can provide a useful tool for prenatal monitoring and management.

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References

Ayres-de-Campos D, Bernardes J. Fetal Monitoring: The Old and the New. Best Pract Res Clin Obstet Gynae Col, 2016, 30: 3 - 12.

Oweis R J, Haddad F S, Kanaan M. Fetal Acidosis Prediction Using Random Forest Algorithm. Int J Med Inform, 2019.

Tharwat A, Gaber T, Hassanien A E. A Hybrid Model for Fetal Distress Classification. Comput Biol Med, 2017, 81: 67 - 75.

Wang Y, Yao Y, Lu X. Fetal Distress Detection by Convolutional Neural Network. IEEE Access, 2019, 7: 6607 - 36614.

Ren H, Zhao T, Wang L, et al. Fetal Hypoxia Detection by Deep Belief Network with Intrapartum Cardiotocography. J Matern Fetal Neonatal Med, 2020, 33 (4): 558 - 565.

Shoushtarian M, Hajizadeh S, Khalilzadeh MA, et al. Correlation-Based Feature Selection for Fetal Distress Classification. Comput Biol Med, 2014, 45: 1 - 8.

Zhang X, Zhou Y, Lin C, Zhao Y. Genetic Algorithm Based Feature Selection for Fetal Distress Classification. Comput Biol Med, 2015, 59: 42 - 51.

Gutiérrez-Ríos R, González-Lorenzo M, Alcalá-Fdez J, et al. Fetal Health Classification by Means of Data Augmentation and Random Forests. PLoS One, 2019, 14 (4): e0215154.

Hu J, Li Y, Yan J, Li M, He X. Explainable Artificial Intelligence for Fetal Distress Prediction. BMC Med Inform Decis Mak, 2020, 20 (1): 192.

Ilea D E, Siriteanu T, Kovacs L. Fetal health classification using support vector machines and neural networks. 2012 IEEE 10th International Symposium on Intelligent Systems and Informatics, 2012.

Ayres de Campos et al. SisPorto 2.0 A Program for Automated Analysis of Cardiotocograms. J Matern Fetal Med, 2000, 5: 311 - 318.