Automated approach for fetal and maternal health management using light gradient boosting model with SHAP explainable AI
Bibliographic Data
| ID | 22069052 |
|---|---|
| Authors | Nisreen Innab (0000-0003-4412-7727, Almaarefa University), Shtwai Alsubai (0000-0002-6584-7400, Prince Sattam Bin Abdulaziz University), Ebtisam Abdullah Alabdulqader (0000-0002-8539-5560, King Saud University), Aisha Ahmed Alarfaj (0000-0002-5078-2579, Princess Nourah bint Abdulrahman University), Muhammad Umer (0000-0001-9334-3261, Islamia University of Bahawalpur), Silvia Treľová (0000-0003-4851-6666, Comenius University Bratislava, corresponding author), Imran Ashraf (0000-0002-5632-5790, Yeungnam University, corresponding author) |
| Year | 2024 |
| Volume | 12 |
| Pages | 1462693-1462693 |
| Publication date | 2024-12-20 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2024.1462693 |
| PMID | 39758195 |
| OpenAlex | W4405652559 |
| Language | EN |
| References cited | 42 |
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
Gradient boosting · Machine learning · Random forest · Artificial Intelligence in Healthcare · Computer Science · Imbalanced Data Classification Techniques · Machine Learning in Healthcare · Medicine · Artificial Intelligence
Applied Logistic Regression
An introduction to ROC analysis
The relationship between Precision-Recall and ROC curves
Extremely randomized trees
Support-Vector Networks
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
A systematic analysis of performance measures for classification tasks
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
Smote
Nearest neighbor pattern classification
Greedy function approximation
Random Forests
A Coefficient of Agreement for Nominal Scales
| Highly cited | No |
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