Application and Evaluation of Machine Learning Algorithms in Classifying Cardiotocography (CTG) Signals

Abstract

Cardiotocography (CTG) is a clinical procedure performed to monitor fetal health by recording uterine contractions and the fetal heart rate continuously. This procedure is carried out mainly in the third trimester of pregnancy. This work aims at proving the significance of upsampling the data using SMOTE (Synthetic Minority Oversampling Technique) in classifying the CTG traces. The project includes the comparison of different Machine Learning approaches, namely, Logistic Regression, Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Random Forest, and Knearest Neighbor (KNN) classifiers on the CTG dataset to classify the records into three classes: normal, suspicious and pathological. The results prove that applying SMOTE increases the performance of the classifiers.