Early Detection of Fetal Health Conditions Using Machine Learning for Classifying Imbalanced Cardiotocographic Data

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dc.authorid0000-0002-1588-8220
dc.authorid0000-0001-7086-6182
dc.authorid0000-0001-7829-8087
dc.contributor.authorNazli, Irem
dc.contributor.authorKorbeko, Ertugrul
dc.contributor.authorDogru, Seyma
dc.contributor.authorKugu, Emin
dc.contributor.authorSahingoz, Ozgur Koray
dc.date.accessioned2026-02-08T15:15:55Z
dc.date.available2026-02-08T15:15:55Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractBackground: Cardiotocography (CTG) is widely used in obstetrics to monitor fetal heart rate and uterine contractions. It helps detect early signs of fetal distress. However, manual interpretation of CTG can be time-consuming and may vary between clinicians. Recent advances in machine learning provide more efficient and consistent alternatives for analyzing CTG data. Objectives: This study aims to investigate the classification of fetal health using various machine learning models to facilitate early detection of fetal health conditions. Methods: This study utilized a tabular dataset comprising 2126 patient records and 21 features. To classify fetal health outcomes, various machine learning algorithms were employed, including CatBoost, Decision Tree, ExtraTrees, Gradient Boosting, KNN, LightGBM, Random Forest, SVM, ANN and DNN. To address class imbalance and enhance model performance, the Synthetic Minority Oversampling Technique (SMOTE) was employed. Results: Among the tested models, the LightGBM algorithm achieved the highest performance, boasting a classification accuracy of 90.73% and, more notably, a balanced accuracy of 91.34%. This superior balanced accuracy highlights LightGBM's effectiveness in handling imbalanced datasets, outperforming other models in ensuring fair classification across all classes. Conclusions: This study highlights the potential of machine learning models as reliable tools for fetal health classification. The findings emphasize the transformative impact of such technologies on medical diagnostics. Additionally, the use of SMOTE effectively addressed dataset imbalance, further enhancing the reliability and applicability of the proposed approach.
dc.identifier.doi10.3390/diagnostics15101250
dc.identifier.issn2075-4418
dc.identifier.issue10
dc.identifier.pmid40428243
dc.identifier.scopus2-s2.0-105006481704
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/diagnostics15101250
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6046
dc.identifier.volume15
dc.identifier.wosWOS:001496154900001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofDiagnostics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzWOS_KA_20260207
dc.subjectcardiotocography
dc.subjectfetal health
dc.subjectmachine learning
dc.subjectmedical diagnostics
dc.titleEarly Detection of Fetal Health Conditions Using Machine Learning for Classifying Imbalanced Cardiotocographic Data
dc.typeArticle

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