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.
Keywords: Classification algorithms, CTG, Decision Tree, Fetal Heart Rate, K-nearest Neighbors, Logistic Regression, Naïve Bayes, Random Forest, Support Vector Machine.
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