Cardiotocography Analysis Using Conjunction of Machine Learning Algorithms

Fetal distress is a primary factor for cesarean section in obstetrics and gynecology. If there is a lack of oxygen for the fetus in the uterus, then there is always a risk of deteriorating health which could lead to death as well. Cardiotocography (CTG) the most popular technique to observe fetal health. Fetalheart rate (FHR) is an essential index to identify occurences of fetal distress. This study makes use of the following to evaluate fetal distress: 1) Decision tree (DT) 2) Support Vector Machines (SVM) 3) Random Forest 4) Neural Networks 5) Gradient Boosting We present in this paper a new algorithm which improves the accuracy to 99.25% which is higher than what was obtained inprevious research.