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All Journal Indonesian Journal of Applied Technology and Innovation Science
Khairunnisa, Kharisma Ummi
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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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 Vol. 3 No. 1 (2026): IJATIS February 2026
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v3i1.2496

Abstract

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.
Co-Authors Alhady, M. Naufal Dzaky Mahmudah, Arifah Nur Mustika, Dinda Putri, Rindiani Suhadi