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ORIGINAL RESEARCH article

Front. Public Health
Sec. Children and Health
Volume 12 - 2024 | doi: 10.3389/fpubh.2024.1462693
This article is part of the Research Topic Early Maternal and Child Health Management and the Impact of Living Environment View all 8 articles

Automated Approach For Fetal & Maternal Health Management Using Light Gradient Boosting Model With SHAP Explainable AI

Provisionally accepted
Muhammad Umer Muhammad Umer 1*Nisreen Innab Nisreen Innab 2Shtwai Alsubai Shtwai Alsubai 3Ebtisam Abdullah Alabdulqader Ebtisam Abdullah Alabdulqader 4Aisha Ahmed Alarfaj Aisha Ahmed Alarfaj 5Silvia Treľová Silvia Treľová 6*Imran Ashraf Imran Ashraf 7
  • 1 Islamia University of Bahawalpur, Bahawalpur, Pakistan
  • 2 University of Almaarefa, Riyadh, Saudi Arabia
  • 3 Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
  • 4 King Saud University, Riyadh, Riyadh, Saudi Arabia
  • 5 Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
  • 6 Comenius University, Bratislava, Bratislava, Slovakia
  • 7 Yeungnam University, Gyeongsan, North Gyeongsang, Republic of Korea

The final, formatted version of the article will be published soon.

    Fetal health holds paramount importance in prenatal care and obstetrics, as it directly impacts the well-being of mother and fetus. Monitoring fetal health through pregnancy is crucial for identifying and addressing potential risks and complications that may arise. Early detection of abnormalities and deviations in fetal health can facilitate timely interventions to mitigate risks and improve outcomes for the mother and fetus. Monitoring fetal health also provides valuable insights into the effectiveness of prenatal interventions and treatments. For fetal health classification, this research work makes use of cardiotocography (CTG) data containing 21 features including fetal growth, development, and physiological parameters such as heart rate and movement patterns with three target classes 'normal', 'suspect', and 'pathological'. The proposed methodology makes use of data upsampled using the synthetic minority oversampling technique (SMOTE) to handle the class imbalance problem that is very crucial in medical diagnosing with a light gradient boosting machine. The results show that the proposed model gives 0.9989 accuracy, 0.9988 area under the curve, 0.9832 recall, 0.9834 precision, 0.9832 F1 score, 0.9748 Kappa score, and 0.9749 Matthews correlation coefficient value on the test dataset. The performance of the proposed model is compared with other machine learning models to show the dominance of the proposed model. The proposed model's significance is further evaluated using 10-fold cross-validation and comparing the proposed model with other state-of-the-art models.

    Keywords: Public Health, risk perceptions, healthcare, Mother and child care, machine learning

    Received: 10 Jul 2024; Accepted: 04 Dec 2024.

    Copyright: © 2024 Umer, Innab, Alsubai, Alabdulqader, Alarfaj, Treľová and Ashraf. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Muhammad Umer, Islamia University of Bahawalpur, Bahawalpur, Pakistan
    Silvia Treľová, Comenius University, Bratislava, 818 06, Bratislava, Slovakia

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.