| Summary: | The umbilical cord is an organ that circulates oxygen and nutrition from mother to fetus during
pregnancy. This study aims to classify the umbilical cord based on ultrasound images. The similarity
of shape and coil between each class becomes a challenge. Therefore, it requires feature values that
are relevant to the characteristics of these three classes. The condition of imbalanced data sets in
this study is also an obstacle that causes the classifier’s performance to degrade on minority classes.
Therefore, this study proposes a machine learning model capable of properly dealing with imbalanced
data sets and recognizing the umbilical cord class
Furthermore, this study proposes a new feature extraction method, namely, the umbilical coiling
index (UCI), which directly adopts obstetricians’ knowledge. The proposed model consists of five
stages: image preprocessing, feature extraction, feature selection, oversampling data using SMOTE,
and Classification. Machine learning method observations were carried out comprehensively on five
based classifiers: Random Forest, KNN, Decision tree, SVM, Na¨ıve Bayes, and Multiclassifier. The
results showed that the Random forest and Multiclassifier methods provide the highest accuracy,
precision, recall, and F-measure performance in imbalanced data sets.
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