Automated approach for fetal and maternal health management using light gradient boosting model with SHAP explainable AI
Fetal health holds paramount importance in prenatal care and obstetrics, as it directly impacts the wellbeing 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 an...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2024.1462693/full |
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| author | Nisreen Innab Shtwai Alsubai Ebtisam Abdullah Alabdulqader Aisha Ahmed Alarfaj Muhammad Umer Silvia Trelova Imran Ashraf |
| author_facet | Nisreen Innab Shtwai Alsubai Ebtisam Abdullah Alabdulqader Aisha Ahmed Alarfaj Muhammad Umer Silvia Trelova Imran Ashraf |
| author_sort | Nisreen Innab |
| collection | DOAJ |
| description | Fetal health holds paramount importance in prenatal care and obstetrics, as it directly impacts the wellbeing 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. |
| format | Article |
| id | doaj-art-3ebb121e7ccd4885bf8a5bde48be66f9 |
| institution | DOAJ |
| issn | 2296-2565 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj-art-3ebb121e7ccd4885bf8a5bde48be66f92025-08-20T02:52:15ZengFrontiers Media S.A.Frontiers in Public Health2296-25652024-12-011210.3389/fpubh.2024.14626931462693Automated approach for fetal and maternal health management using light gradient boosting model with SHAP explainable AINisreen Innab0Shtwai Alsubai1Ebtisam Abdullah Alabdulqader2Aisha Ahmed Alarfaj3Muhammad Umer4Silvia Trelova5Imran Ashraf6Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Diriyah, Riyadh, Saudi ArabiaDepartment of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Information Technology, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Computer Science and Information Technology, The Islamia University of Bahawalpur, Bahawalpur, PakistanFaculty of Management, Comenius University Bratislava, Bratislava, SlovakiaDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan, Republic of KoreaFetal health holds paramount importance in prenatal care and obstetrics, as it directly impacts the wellbeing 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.https://www.frontiersin.org/articles/10.3389/fpubh.2024.1462693/fullpublic healthrisk perceptionshealthcaremother and child caremachine learning |
| spellingShingle | Nisreen Innab Shtwai Alsubai Ebtisam Abdullah Alabdulqader Aisha Ahmed Alarfaj Muhammad Umer Silvia Trelova Imran Ashraf Automated approach for fetal and maternal health management using light gradient boosting model with SHAP explainable AI Frontiers in Public Health public health risk perceptions healthcare mother and child care machine learning |
| title | Automated approach for fetal and maternal health management using light gradient boosting model with SHAP explainable AI |
| title_full | Automated approach for fetal and maternal health management using light gradient boosting model with SHAP explainable AI |
| title_fullStr | Automated approach for fetal and maternal health management using light gradient boosting model with SHAP explainable AI |
| title_full_unstemmed | Automated approach for fetal and maternal health management using light gradient boosting model with SHAP explainable AI |
| title_short | Automated approach for fetal and maternal health management using light gradient boosting model with SHAP explainable AI |
| title_sort | automated approach for fetal and maternal health management using light gradient boosting model with shap explainable ai |
| topic | public health risk perceptions healthcare mother and child care machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2024.1462693/full |
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