What question did this study set out to answer?

This research aims to evaluate the effectiveness of machine learning models in predicting total bilirubin levels in newborns.

May 1, 20260 citations

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Yenidoğanlarda Total Bilirubin Tahmininde Makine Öğrenmesi Modellerinin Klinik Uygulanabilirliği Üzerine

SCSemih Musa Coşkun MŞMelih Şengül

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.

Abstract

Dear Editor,We read with great interest the article entitled “Prediction of Total Bilirubin Levels in Newborns Using Machine Learning”. Machine learning -based approaches for the early identification of neonatal hyperbilirubinemia represent an important and evolving field within clinical decision support systems.In the study, multiple ML algorithms were compared, and the Gradient Boosting model demonstrated the highest accuracy and AUC values in the test dataset . However, the dataset exhibited a marked class imbalance (

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Yenidoğanlarda Total Bilirubin Tahmininde Makine Öğrenmesi Modellerinin Klinik Uygulanabilirliği Üzerine

Key Points

Abstract

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