A Novel Hybrid Model For Automated Analysis Of Cardiotocograms Using Machine Learning Algorithms

Abstract

In this study, a new hybrid model was presented for the prediction of fetal state from fetal heart rate (FHR) and the uterine contraction (UC) signals obtained from cardiotocogram (CTG) recordings. CTG monitoring of FHR and uterine contractions during pregnancy and delivery provides information on the physiological status of the fetus to identify hypoxia. Accurate information from these records can be used to estimate the pathological condition of the fetus. Thus, it allows early intervention by reporting any irreversible negative condition in the fetus. In this study, due to the importance of this subject, a new hybrid model was developed which can perform high rate accurate diagnosis using Machine Learning (ML) algorithms. In the hybrid model, 4 different ML algorithms (k Nearest Neighbors (k-NN), Decision Tree (DT), Naive Bayes (NB) and Support Vector Machine (SVM)) were used. While the diagnosis without the hybrid model was low, the improved hybrid model increased the accuracy by 34%. As a result of this hybrid model, 100% success was achieved for classification, test success, Accuracy, Sensitivity and Specificity with NB and DT ML algorithms.