Impact of Classification Algorithms on Cardiotocography Dataset for Fetal State Prediction

Abstract

Monitoring of fetal heart rate and fetal health is done by cardiotocography (CTG). Obstetricians can observe CTG records and make life-saving decisions. The ability to go throh all the data points is fairly challenging. One possible solution is to use clinical decision making systems. The selection of these systems is made possible by choosing the best classifier, in this paper we compare four simple classifiers (K Nearest Neighbors, Decision Tree, Support Vector Machine, Naive Bayes). To improve accuracy, the dataset is split based on “Outlier Removal” and “Feature Selection”.