Fetal Health Risk Classification using Important Feature Selection and CART Model on Cardiotocography Data - Ilham, Ahmad; Nagara, Thahta Ardhika Prabu; Kamaruddin, Mudyawati; Khikmah, Laelatul; Mantoro, Teddy (Informatica, 2025)
Fetal mortality and newborn health issues require urgent attention due to high maternal and infant mortality rates during labor, highlighting the critical need for accurate fetal condition monitoring to reduce complications. This study proposes the development of a fetal health risk classification model based on Important Feature Selection (IFS) and Classification and Regression Tree (CART) using cardiotocography (CTG) data from the UCI Machine Learning Repository. The IFS method was used to select the most relevant features, reduce model complexity, and increase generalization to prevent overfitting. The IFS-CART model was tested with 10-fold cross-validation and showed an accuracy of 94.50%, superior to the conventional CART which only reached 93.83%. In addition, the average value of True Positive Rate (TPR) and True Negative Rate (TNR) also increased, indicating that this model is effective in distinguishing normal, suspected, and pathological fetal conditions. Evaluation using the Area Under the Curve - Receiver Operating Characteristic (AUC-ROC) showed that the model has high performance in detecting at-risk conditions with an AUC of 0.981 for the "suspect" class. This finding confirms that IFS-CART is not only accurate, but also has high interpretability, making it easy for medical personnel to use for clinical decision support. The results of this study shows that IFS-CART can serve as a reliable decision support system in real-time fetal health monitoring. Further implementation is expected to improve diagnosis accuracy and prevent complications during pregnancy and labor.