Abstract
Contractions (UC) and Fetal Heart Rate (FHR) are the most common techniques for evaluating fetal and maternal assessment during pregnancy and detecting the changes in fetal oxygenation occurred throughout labor. By monitoring the Cardiotocography (CTG) patterns, doctors can measure fetus state, accelerations, heart rate, and uterine contractions. Several computational and machine learning (ML) methods have been done on CTG recordings to improve the effectiveness of fetus analysis and aid the doctors to understand the variations in their interpretation. However, getting an optimal solution and best accuracy remains an important concern. Among the various ML approaches, artificial neural network (ANN)-based approach has achieved a high performance in several applications. In this paper, an optimized Single Layer Perceptron (SLP)-based approach is proposed to classify the CTG data accurately and predict the fetal state. The approach is able to exploit the advantages of SLP model and optimize the learning rate using a grid search method in which we can arrive at the best accuracy and converge to a local minima. The approach is evaluated on CTG dataset of University of California, Irvine (UCI). The optimized SLP model is trained and tested on the dataset using a 10-fold cross-validation technique to classify the CTG patterns as normal, suspect or pathologic. The experimental results show that the proposed approach achieved 99.20% accuracy compared with the state-of-the-art models.