A Methodological Study on Fetal Health Classification Using Optimized LightGBM with SMOTE and Optuna-Based Hyperparameter Optimization

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dc.contributor.authorYalcin, Emre
dc.contributor.authorAslan, Serpil
dc.contributor.authorTanyildiz, Hayriye
dc.date.accessioned2026-06-19T06:37:30Z
dc.date.available2026-06-19T06:37:30Z
dc.date.issued2025
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractObjective: Fetal health classification is of great clinical importance as it allows the early detection and management of fetal health problems during pregnancy.This study aims to enhance fetal health classification by integrating Light Gradient Boosting Machine (LightGBM), synthetic minority oversampling technique (SMOTE), and Optuna-based hyperparameter tuning. The goal is to improve classification accuracy, address class imbalance, and optimize model performance in predicting normal, suspicious, and pathological fetal health conditions. Material and Methods: The study utilized the University of California, Irvine Cardiotocography Data Set which contains 2.126 fetal cardiotocography (CTG) records classified into 3 categories. To handle class imbalance, SMOTE is applied. Various machine learning models [Stochastic Gradient Descent Classifier, Gradient Boosting, Decision Tree, Support Vector Machine, K-Nearest Neighbors, and Logistic Regression (LG)] were compared, with LightGBM selected due to its efficiency in structured medical data processing. Optuna is used for hyperparameter tuning. The dataset was split into 80% training and 20% testing, and model performance was assessed using accuracy, precision, recall, and F1-score. Results: The optimized LightGBM+SMOTE+Optuna model achieved 99% accuracy, significantly outperforming the other classifiers. The recall and F1-score for the suspicious and pathological classes improved, reducing the misclassification rates. The confusion matrix confirmed a substantial decrease in errors, demonstrating the model's robustness and reliability. Conclusion: The proposed model successfully enhances fetal health classification accuracy by addressing class imbalance and optimizing hyperparameters. The integration of LightGBM, SMOTE, and Optuna improves model generalization, making it a valuable tool for fetal health assessment. Future studies could explore deep learning approaches to further refine classification performance and support clinical decision-making.
dc.identifier.doi10.5336/jcog.2025-110259
dc.identifier.endpage122
dc.identifier.issn2619-9467
dc.identifier.issue3
dc.identifier.scopus2-s2.0-105034387386
dc.identifier.scopusqualityQ4
dc.identifier.startpage115
dc.identifier.urihttps://doi.org/10.5336/jcog.2025-110259
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5060
dc.identifier.volume35
dc.identifier.wosWOS:001619095300005
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTurkiye Klinikleri
dc.relation.ispartofJournal of Clinical Obstetrics and Gynecology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260612
dc.subjectFetal Monitoring
dc.subjectMachine Learning
dc.subjectPregnancy
dc.titleA Methodological Study on Fetal Health Classification Using Optimized LightGBM with SMOTE and Optuna-Based Hyperparameter Optimization
dc.typeArticle

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