Early Detection of Fetal Distress using CTG and Machine Learning to Improve Maternal and Child Health
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Abstract
This study addresses the critical challenge of fetal distress identification, with either maternal or neonatal death coming first in this case. Manual CTG interpretation, being subjective, leads to disadvantages, and a high degree of inter-observer variability may result in misdiagnosis or late clinical intervention with detrimental consequences. Thus, our research fills the gap wherein there has been very little comprehensive and rigorous comparative analysis of a large number of machine learning models. We systematically studied a wide and diverse range of classifiers in our comparison, including tree-based ensembles such as XGBoost, Random Forest, CatBoost, and LightGBM, but also Gradient Boosting, Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression, AdaBoost, Naive Bayes, and Convolutional Neural Networks (CNN). These models were trained and tested on a very large and open-source CTG dataset to validate their predictive power. Our findings reveal that the XGBoost model demonstrated superior performance with an impressive accuracy of 99.04%, while CatBoost, LightGBM, and Random Forest, which had intense predictive powers well above traditional diagnostic means. The above-mentioned accuracy-driven models have found their aptitude in capturing highly complicated nonlinear patterns occurring in CTG data and therefore hold a promising prospect to be developed and applied toward a large-scale and automated diagnostic aid. The successful implementation of these novel techniques would have huge potential for improving the quality of prenatal care and clinical decision-making in resource-poor areas, where expert supervision may be widely scarce.
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