Abstract: Automatic Fetal Health Prediction plays a vital role in supporting early prenatal intervention through continuous and non-invasive monitoring. Recent advances in biocompatible sensors enable the safe long-term acquisition of physiological signals, which can be effectively analyzed using machine learning techniques. This study proposes a comprehensive machine learning pipeline for Fetal Health Prediction through fetal health classification using the fetal_health.csv dataset from Kaggle, consisting of 2,126 samples and 22 cardiotocography-derived features related to fetal heart rate and uterine contractions. To address class imbalance and the presence of outliers, RobustScaler normalization was applied during the preprocessing stage. Feature selection was performed using Random Forest feature importance to identify the most relevant predictors. Two classification models, namely Random Forest (RF) and Support Vector Machine (SVM), were trained and evaluated using an 80:20 stratified train–test split. Experimental results indicate that the Random Forest model outperformed SVM, achieving an accuracy of 92.7% and a macro F1-score of 85.9%, compared with 88.97% accuracy and a macro F1-score of 79.85% for SVM. Moreover, Random Forest demonstrated superior performance in detecting minority classes (Suspect and Pathological), which are of high clinical significance. These findings suggest that the proposed pipeline is robust, interpretable, and suitable for integration with biocompatible sensor-based systems for real-time fetal health monitoring and clinical decision support.

Yuli Wahyuni, Hadiyanto, Ridwan Sanjaya and Nendar Herdianto. “Optimizing Fetal Health Prediction Using Machine Learning on Biocompatible Sensor Data”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.12 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161291

@article{Wahyuni2025,
title = {Optimizing Fetal Health Prediction Using Machine Learning on Biocompatible Sensor Data},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161291},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161291},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {12},
author = {Yuli Wahyuni and Hadiyanto and Ridwan Sanjaya and Nendar Herdianto}
}