Prediction of fetal state from the cardiotocogram recordings using neural network models

The combination of machine vision and soft computing approaches in the clinical decisions, using training data, can improve medical decisions and treatments. The cardiotocography (CTG) monitoring and uterine activity (UA) provides useful information about the condition of the fetus and the cesarean or natural delivery. The visual assessment by the pathologists takes a lot of time and may be incompatible. Therefore, creating a computer intelligent method to assess fetal wellbeing before the mother labour is very important. In this study, many diverse approaches are suggested for predicting fetal state classes based on artificial intelligence. The various topologies of multi-layer architecture of a sub-adaptive neuro fuzzy inference system (MLA-ANFIS) using multiple input features, neural networks (NN), deep stacked sparse auto-encoders (DSSAEs), and deep-ANFIS models are implemented on a CTG data set. Experimental results contributing to DSSAE are more accurate than other suggested techniques to predict fetal state. The proposed method achieved a sensitivity of 99.716, specificity of 97.500 and geometric mean of 98.602 with accuracy of 99.503.