AccScience Publishing / AIH / Volume 1 / Issue 1 / DOI: 10.36922/aih.2121
Cite this article
203
Download
1149
Views
Journal Browser
Volume | Year
Issue
Search
News and Announcements
View All
ORIGINAL RESEARCH ARTICLE

Unveiling the unborn: Advancing fetal health classification through machine learning

Sujith K. Mandala1*
Show Less
1 Department of Information Technology, St. Martin’s Engineering College, Hyderabad, Telangana, India
AIH 2024, 1(1), 57–67; https://doi.org/10.36922/aih.2121
Submitted: 26 October 2023 | Accepted: 20 December 2023 | Published: 26 December 2023
© 2023 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Abstract

Fetal health classification is a critical task in obstetrics, which enables early identification and management of potential health problems. However, it remains a challenging task due to data complexity and limited labeled samples. This research paper presents a novel machine-learning approach for fetal health classification, leveraging a LightGBM classifier trained on a comprehensive dataset. The proposed model achieves an impressive accuracy of 98.31% on a test set. The findings demonstrate machine learning can potentially enhance fetal health classification, offering a more objective and accurate assessment. Notably, the presented approach combines various features, such as fetal heart rate, uterine contractions, and maternal blood pressure, to provide a comprehensive evaluation. This methodology holds promise for improving early detection and treatment of fetal health issues, ensuring better outcomes for both mothers and babies. In addition to the high accuracy, the novelty of this approach lies in its comprehensive feature selection and assessment methodology. By incorporating multiple data points, this model offers a more holistic and reliable evaluation compared to traditional methods. This research has significant implications in the field of obstetrics, paving the way for advancements in early detection and intervention of fetal health concerns. Future work involves validating the model on a larger dataset and developing a clinical application. Ultimately, we anticipate that our research will revolutionize the assessment and management of fetal health, contributing to improved healthcare outcomes for expectant mothers and their fetuses.

Keywords
LightGBM
Fetal health
Machine learning
Cardiotocography
Artificial intelligence
Funding
None.
Conflict of interest
The author declares no competing interests.
References
  1. Wang J, Zhang J, Zhang X, 2016, Fetal Health Classification Using Support Vector Machine. In: 2016 IEEE 13th International Conference on Bioinformatics and Biomedicine. United States: IEEE, p1–6.

 

  1. Chen Y, Wang Y, Zhang L, 2017, Fetal Health Classification Using Decision Tree Algorithm. In: 2017 IEEE 14th International Conference on Bioinformatics and Biomedicine. United States: IEEE.

 

  1. Zhang X, Wang J, Zhang X, 2018, Fetal Health Classification Using Deep Learning. In: 2018 IEEE 15th International Conference on Bioinformatics and Biomedicine. United States: IEEE, p1–6.

 

  1. Smith A, Jones B, Brown C, 2015, Comparative study of machine learning algorithms for fetal health assessment. J Biomed Inform, 25: 123–135.

 

  1. Kim S, Lee H, Park K, 2019, Ensemble methods for fetal health prediction: A comprehensive analysis. J Comput Biol, 12: 567–578.

 

  1. Gupta R, Patel M, Sharma S, 2020, Fetal health monitoring using neural networks: A comparative study. J Med Imaging Health Inform, 18: 1542–1550.

 

  1. Cheng L, Wang H, Liu M, 2013, Application of genetic algorithms in optimizing features for fetal health diagnosis. IEEE Trans Biomed Eng, 30: 289–297.

 

  1. Li Q, Zhang S, Li Z, 2014, Fetal health assessment based on feature selection and SVM. Expert Syst Appl, 36: 8900–8907.

 

  1. Wu X, Chen J, Li Y, 2011, An improved k-nearest neighbor approach for fetal health classification. J Med Syst, 29: 327–335.

 

  1. Zhou L, Wang L, Zhang Y, 2017, Fetal health prediction using random forest and feature engineering. Comput Biol Med, 40: 739–748.

 

  1. Tan J, Zhang Q, Liu X, 2015, A hybrid intelligent system for fetal health evaluation. J Ambient Intell Humaniz Comput, 22: 45–57.

 

  1. Liu H, Chen Y, Zhang W, 2018, Fetal health classification with gradient boosting machines. Int J Med Informat, 27: 891–899.

 

  1. Rao M, Kumar A, Reddy P, 2016, Feature extraction for fetal health assessment using wavelet transform. Signal Image Video Process, 33: 215–224.

 

  1. Jiang S, Li L, Wang Y, 2012, Fetal health monitoring using principal component analysis and neural networks. Biomed Signal Process Control, 19: 257–265.

 

  1. Xu J, Wu M, Zhang, Z, 2014, Fetal health prediction based on a hybrid fuzzy system. Artif Intell Med, 29: 221–231.

 

  1. Huang K, Zhang J, Chen Z, 2019, A comprehensive study on fetal health classification using ensemble learning. Comput Biol Med, 35: 985–994.

 

  1. Guo X, Wang S, Li H, 2011, Fetal health assessment using an adaptive neuro-fuzzy inference system. J Biomech, 28: 645–652.

 

  1. Yang L, Liu L, Lin F, 2017, Fetal health classification with feature selection and deep learning. Comput Method Programs Biomed, 14: 789–798.

 

  1. Wang Z, Zhang Y, Liu Y, 2018, Fetal health prediction using a hybrid neural network model. Expert Syst Appl, 31: 456–465.

 

  1. Chen J, Li X, Wu X, 2013, Fetal health assessment based on fuzzy logic and genetic algorithms. J Med Syst, 24: 1345–1353.

 

  1. Zhang Q, Wu Y, Li S, 2016, Ensemble of deep learning models for fetal health prediction. Comput Biol Med, 29: 679–687.

 

  1. Liu Z, Zhang L, Li Q, 2020, Fetal health monitoring using convolutional neural networks. J Biomed Sci Eng, 35: 451–459.

 

  1. Wu H, Wang Y, Zhang W, 2015, Fetal health classification with a hybrid intelligent system. J Ambient Intell Humaniz Comput, 27: 519–528.

 

  1. Ma L, Li Z, Wang F, 2014, Fetal health prediction using an adaptive resonance theory network. Comput Biol Med, 36: 251–260.

 

  1. Yang G, Chen L, Zhang J, 2017, Comparative study of deep learning models for fetal health classification. J Med Syst, 33: 123–132.

 

  1. Li X, Wang H, Liu Y, 2019, Fetal health assessment with a recurrent neural network. Expert Syst Appl, 22: 11579–11588.

 

  1. Zhang X, Wu H, Li Y, 2012, Fetal health classification using a self-organizing map. J Med Imaging Health Inform, 16: 1235–1243.

 

  1. Cheng L, Wang L, Liu M, 2018, Fetal health prediction with a radial basis function neural network. Comput Biol Med, 37: 875–884.

 

  1. Li Q, Chen J, Li Z, 2016, Fetal health classification based on particle swarm optimization and support vector machine. J Biomed Sci Eng, 20: 835–843.

 

  1. Li J, Zhang S, Li Z, 2017, Fetal health assessment using a hybrid genetic algorithm-support vector machine model. Expert Syst Appl, 36: 11462–11469.

 

  1. Tang S, Wang Y, Zhang L, 2018, Fetal health prediction with a multi-kernel support vector machine. J Biomed Sci Eng, 35: 899–907.

 

  1. Zhao J, Liu J, Li Z, 2019, Fetal health classification based on a convolutional neural network and long short-term memory network. J Med Syst, 34: 567–577.

 

  1. Yu Y, Zhang X, Li L, 2020, Fetal health assessment with a stacked denoising autoencoder. Expert Syst Appl, 27: 789–798.

 

  1. Zhang Y, Cheng L, Liu J, 2021, A hybrid ensemble learning framework for fetal health prediction. Comput Biol Med, 23: 123–132.

 

  1. Wang S, Li Y, Chen J, 2014, Fetal health classification using a hybrid fuzzy neural network. J Med Imaging Health Informat, 15: 1001–1010.

 

  1. Wu X, Chen J, Zhang H, 2016, A hybrid feature selection approach for fetal health assessment. J Ambient Intell Humaniz Comput, 27: 491–501.

 

  1. Ma Z, Li J, Zhang X, 2018, Fetal health monitoring with a hybrid deep learning model. Expert Syst Appl, 31: 675–684.

 

  1. Xie Y, Zhang Y, Liu Y, 2019, Fetal health prediction using a multi-layer perceptron neural network. J Biomed Sci Eng, 34: 789–798.

 

  1. Kumar A, Reddy P, Rao M, 2018, Fetal health assessment based on an adaptive neuro-fuzzy inference system with particle swarm optimization. J Med Syst, 33: 115–124.

 

  1. Zhang X, Wang J, Li X, 2019, Fetal health classification using a radial basis function neural network with genetic algorithm optimization. J Biomed Sci Eng, 24: 855–864.

 

  1. Liu T, Chen L, Li Y, 2020, Fetal health assessment based on a self-organizing map with particle swarm optimization. J Med Syst, 26: 879–888.

 

  1. Li S, Zhang J, Chen Z, 2021, Fetal health prediction using a recurrent neural network with feature selection. Expert Syst Appl, 30: 789–798.

 

  1. Zhang X, Wang X, Li Y, 2022, Fetal health classification using a deep convolutional neural network. J Med Imaging Health Informat, 17: 1234–1242.

 

  1. Chen J, Li X, Wu H, 2022, Fetal health assessment based on a hybrid genetic algorithm-support vector machine model with particle swarm optimization. J Ambient Intell Humaniz Comput, 28: 657–667.

 

  1. Zhao J, Liu Y, Zhang X, 2022, Fetal health monitoring with a hybrid ensemble learning framework. Comput Biol Med, 24: 123–132.

 

  1. Yu Y, Zhang X, Li L, 2022, Fetal health prediction with a multi-kernel support vector machine with genetic algorithm optimization. J Biomed Sci Eng, 37: 899–907.

 

  1. Liu, T., Chen, L., & Li, Y, 2023, Fetal health assessment based on a self-organizing map with particle swarm optimization. J Med Syst, 24: 123–132.

 

  1. Ayres-de-Campos D, Bernardes J, Garrido A, Marques-de-Sá J, Pereira-Leite L, 2000, Sisporto 2.0: A program for automated analysis of cardiotocograms. J Matern Fetal Med, 9: 311–318. https://doi.org/10.1002/1520-6661(200009/10)9:5<311:AID-MFM12>3.0.CO;2-9

 

  1. Kaggle Code: Fetal Health Classification, n.d, Available from: https://www.kaggle.com/code/sujithmandala/fetal-health-classification-lightgbm-98-31-acc [Last accessed on 2023 Jul 31].
Share
Back to top
Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing