Abstract
Within the medical field, machine learning has the potential to allow doctors and medical professionals to make faster, more accurate diagnoses, empowering specialists to take immediate action. Early diagnosis and prevention of fetal health conditions can be achieved based on the biomarker data derived from the cardiotocography signals. The study proposes using a one-dimensional convolutional neural network for fetal health classification and compares it to conventional machine learning algorithms. A one-dimensional convolutional neural network is shown to outperform traditional machine learning algorithms in both data sets (CTU-CHB and UCI), with an accuracy of 89%-94%.