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February 24, 2026

Yenidoğanlarda Total Bilirubin Tahmininde Makine Öğrenmesi Modellerinin Klinik Uygulanabilirliği Üzerine

Semih Musa Coşkun Melih Şengül

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Key Points

This research aims to evaluate the effectiveness of machine learning models in predicting total bilirubin levels in newborns.
Compared multiple machine learning algorithms for predicting bilirubin levels.
Assessed performance based on accuracy and AUC values in a test dataset.
Identified issues related to class imbalance in the dataset.
The Gradient Boosting model achieved the highest accuracy and AUC values compared to other models.
Notable issues with class imbalance were observed in the dataset.

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Implication

Compares machine learning algorithms to predict total bilirubin in newborns, suggesting Gradient Boosting shows promise.

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Cite This Study

Coşkun et al. (2026) studied this question.

synapsesocial.com/papers/69f4427a967e944ac55660be https://doi.org/10.52309/jaihs.1896978

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