Prediction of Fetal Health Using Machine Learning Algorithms - Mustika, Dinda; Putri, Rindiani Suhadi; Alhady, M. Naufal Dzaky; Khairunnisa, Kharisma Ummi; Mahmudah, Arifah Nur (IJATIS: Indonesian Journal of Applied Technology and Innovation Science, 2026)
This study evaluates several machine learning algorithms for predicting fetal health conditions using cardiotocography (CTG) data. The dataset contains 2,126 records with 22 numerical features obtained from Kaggle and is classified into three categories: normal, suspect, and pathological. Four classification models Decision Tree, Random Forest, Support Vector Machine, and Logistic Regression were implemented and evaluated using two data split scenarios (80:20 and 70:30). Model performance was assessed using precision, recall, and F1-score. The results show that Random Forest achieves the best performance with an F1-score of 91% in both split scenarios, indicating stable and accurate classification compared with other models. The contribution of this study is to provide a comparative evaluation of classical machine learning algorithms for CTG-based fetal health prediction. The findings can support the development of decision-support tools to help medical personnel detect and monitor fetal health risks early.