<![CDATA[This study compares the performance of Random Forest and XGBoost algorithms in classifying fetal health problems using Cardiotocography (CTG) data. The imbalance in the amount of data between classes, the presence of less relevant features, and the challenge in identifying the Suspect class, which has characteristics between the Normal and Pathological classes, are the main problems in the CTG dataset. This condition is important because the early stage of fetal health risk determines further medical treatment represented by the Suspect class. This study uses ANOVA and Mutual Information feature selection techniques, as well as the ADASYN oversampling method to balance the data to overcome these problems. In addition, Random Search is used to optimize model parameters to improve its performance. Unlike previous studies that generally focus on improving accuracy, this study also emphasizes the model's ability to detect minority classes, especially the Suspect class. Based on the results of the study, in almost every test scenario, XGBoost consistently outperforms Random Forest. The XGBoost model obtained optimal accuracy from the combination of ANOVA, ADASYN, and hyperparameter tuning with an accuracy of 95.51%. Meanwhile, the application of Mutual Information with ADASYN and tuning was quite effective in identifying the Suspect class with a higher recall value of 81%. However, because the Suspect class attribute lies between the Normal and Pathological class attributes, the model still faces challenges in optimally distinguishing them. Overall, this study shows that a combination of appropriate feature selection, handling data imbalance, and parameter optimization in a single pipeline can improve model performance more balanced. This research is expected to support more objective medical decision-making, especially in detecting fetal risk conditions from an early stage.]]>
