Classification of Fetal State using Machine Learning Models

In gynecology, the problem of fetus during pregnancy in pregnant women have more interests. literature, several means are used to follow such as cardiotocography measure heart rate, accelerations, fetal movements, and uterine contractions. this proposed study, we use some algorithms classify diseases, confusion matrix specify normal, suspicious pathology using Random Forest, Support Vector Machine, Artificial Neural Network. To validate experimentation, dataset UCI has suggested into three classes: suspicious, pathological best performing model for detecting state is ANN which gave better accuracy values 99.19% training 99.09% test accuracy.