Previously submitted to: JMIR Medical Informatics (no longer under consideration since May 17, 2022)
Date Submitted: Mar 8, 2022
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Deep Neural Networks provide expert level prediction accuracy in classification of fetal state from Cardiotocography records
ABSTRACT
Background:
Cardiotocography (CTG) is a form of electronic fetal monitoring that provides continuous graphic representation of fetal heart rate (FHR) and uterus activity (UA)during pregnancy. CTG is a commonly used method to assess the well-being of the fetuses by clinicians.
Objective:
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Methods:
A publicly available CTG dataset was generated by extracting numerical features from electronic signals of 2126 CTG records that were labeled as normal (n=1655, 77.8%), pathological (n=176, 8.3%) or suspect (n=295, 13.9%) by expert physicians. In this study, we applied Deep Neural Networks (DNNs), Support Vector Classifier (SVC), Random Forest, Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting (LightGBM) classification algorithms to predict normal and pathological fetal states from CTG records.
Results:
Our results show that DNN outperformed conventional ML classifiers with an AUC value of 0.99.
Conclusions:
Considering the unbalanced class distribution, we further applied decision threshold optimization and used the optimized DNN model to provide reliable probability estimates for the fetal state of suspect class samples.
Citation
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