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Use of Machine Learning Algorithms for Prediction of Fetal Risk using Cardiotocographic Data

Background: A major contributor to under‑five mortality is the death of children in the 1st month of life. Intrapartum complications are one of the major causes of perinatal mortality. Fetal cardiotocograph (CTGs) can be used as a monitoring tool to identify high‑risk women during labor. Aim: The objective of this study was to study the precision of machine learning algorithm techniques on CTG data in identifying high‑risk fetuses. Methods: CTG data of 2126 pregnant women were obtained from the University of California Irvine Machine Learning Repository. Ten different machine learning classification models were trained using CTG data. Sensitivity, precision, and F1 score for each class and overall accuracy of each model were obtained to predict normal, suspect, and pathological fetal states. Model with best performance on specified metrics was then identified. Results: Determined by obstetricians’ interpretation of CTGs as gold standard, 70% of them were normal, 20% were suspect, and 10% had a pathological fetal state. On training data, the classification models generated by XGBoost, decision tree, and random forest had high precision (>96%) to predict the suspect and pathological state of the fetus based on the CTG tracings. However, on testing data, XGBoost model had the highest precision to predict a pathological fetal state (>92%). Conclusion: The classification model developed using XGBoost technique had the highest prediction accuracy for an adverse fetal outcome. Lay health‑care workers in low‑ and middle‑income countries can use this model to triage pregnant women in remote areas for early referral and further management

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