Implementation of Random Forest Algorithm with RFE and SMOTE on Cardiotocography Dataset - Nur Taqwimi, Muhammad Ahsani; Wahono, Buang Budi; Mulyo, Harminto (Research Group of Data Engineering, Faculty of Informatics, 2025)
Having a healthy baby is a dream for mothers. However, the high rate of maternal and fetal mortality is still a serious problem, so more accurate fetal health monitoring is needed to prevent pregnancy complications. One of the devices used is Cardiotocography (CTG), which produces data on fetal conditions. The CTG dataset used in this study faces challenges in the form of class imbalance and a high number of features, which can reduce classification performance. This study aims to overcome these challenges by implementing the Random Forest algorithm combined with the Synthetic Minority Oversampling Technique (SMOTE) technique for class balancing and Recursive Feature Elimination (RFE) for feature selection. The dataset used is