AccScience Publishing / AIH / Online First / DOI: 10.36922/aih.2121
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ORIGINAL RESEARCH ARTICLE

Unveiling the unborn: Advancing fetal health classification through machine learning

Sujith K. Mandala1*
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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/ )
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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.
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  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].
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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