Machine learning-based fetal health prediction and development of smart web application

IntroductionFetal health monitoring is critical for early identification of pregnancy-related risks. Manual interpretation of cardiotocography (CTG) signals is subjective and variable among healthcare professionals.MethodsA machine learning-based framework was developed to classify fetal health...

IntroductionFetal health monitoring is critical for early identification of pregnancy-related risks. Manual interpretation of cardiotocography (CTG) signals is subjective and variable among healthcare professionals.MethodsA machine learning-based framework was developed to classify fetal health into Normal, Suspect, and Pathological categories using CTG-derived clinical features. The dataset was preprocessed through duplicate removal, normalization, class balancing using SMOTEENN, multicollinearity analysis via VIF, and Kruskal–Wallis statistical feature selection. Eleven machine learning and neural network models were trained and compared, including Logistic Regression, K-Nearest Neighbors, SVM, Decision Tree, Random Forest, Gradient Boosting, AdaBoost, XGBoost, LightGBM, Multi-Layer Perceptron, and Deep Neural Network.ResultsLightGBM achieved the best overall performance with 96.03% accuracy, 91.99% balanced accuracy, 93.05% macro F1-score, 99.02% ROC-AUC, 88.91% Cohen's Kappa, and 89.01% MCC. SHAP-based explainability identified abnormal short-term variability and fetal heart rate accelerations as the most important features.DiscussionThe best-performing LightGBM model was integrated into a Streamlit-based web application for real-time fetal health prediction, demonstrating its potential as a clinical decision-support tool.

Source: Frontiers AI — Published July 12, 2026 at 23:23 UTC — Category: Research