Yenidoğanlarda Total Bilirubinin Makine Öğrenmesiyle Tahmin Edilmesi

Öz

ÖZET Amaç: Yenidoğan döneminde sık görülen hiperbilirubinemi, zamanında tanı ve tedavi edilmediğinde ciddi nörolojik hasarlara neden olabilir. Bu çalışmanın amacı, makine öğrenmesi (ML) algoritmaları ile yenidoğanlarda total bilirubin düzeylerini tahmin eden modeller geliştirmek ve bu modellerin performansını değerlendirmektir. Gereç ve Yöntem: İzmir Şehir Hastanesi’nde 318 yenidoğana ait 698 örnek retrospektif olarak analiz edilmiştir. Total bilirubin, hematokrit, doğum ağırlığı, gestasyonel yaş, yaş (gün) ve APGAR skoru gibi klinik-demografik veriler kullanılarak sekiz farklı ML algoritması (Gradient Boosting, Random Forest, Naive Bayes, Lojistik Regresyon, Yapay Sinir Ağı vb.) ile sınıflandırma modelleri oluşturulmuştur. Total bilirubin düzeyleri <12.5 ve ≥12.5 mg/dL olarak iki sınıfa ayrılmış, modellerin başarımı 10 kat çapraz doğrulama ile AUC, doğruluk ve F1 skoru gibi metrikler üzerinden değerlendirilmiştir. Model yorumlanabilirliği Decrease in AUC yöntemiyle analiz edilmiştir. Bulgular: Gradient Boosting modeli test veri setinde %92 doğruluk, %0.90 F1 skoru ve 0.89 AUC değeri ile en başarılı model olarak belirlenmiştir. Düşük riskli (<12.5 mg/dL) olgular doğru tahmin edilirken, yüksek riskli (≥12.5 mg/dL) gruplarda hata oranları %90’ın üzerindedir. Değişken önem analizi, yaş (gün), doğum ağırlığı ve gestasyonel yaşın model üzerinde en belirleyici etkiye sahip olduğunu göstermiştir. Sonuç: Makine öğrenmesi algoritmaları, özellikle düşük riskli yenidoğanlarda total bilirubin düzeylerini başarılı şekilde tahmin edebilmektedir. Ancak yüksek riskli grupların doğru tespiti için sınıf dengesizliğini azaltacak yöntemlerin (SMOTE, cost-sensitive learning vb.) kullanılması gerekmektedir.

Anahtar Kelimeler

• Makine öğrenmesi
• yenidoğan sarılığı
• total bilirubin
• Gradient Boosting
• karar destek sistemi

Prediction of Total Bilirubin Levels in Newborns Using Machine Learning

Abstract

ABSTRACT Aim: Neonatal hyperbilirubinemia is a common condition that may lead to severe neurological damage if not diagnosed and treated promptly. This study aimed to develop machine learning (ML) models to predict total bilirubin levels in newborns and evaluate their performance. Material and Method: A total of 698 samples from 318 newborns at İzmir City Hospital were retrospectively analyzed. Clinical and demographic variables, including total bilirubin, hematocrit, birth weight, gestational age, postnatal age (days), and Apgar scores, were used to develop classification models using eight ML algorithms (e.g., Gradient Boosting, Random Forest, Naive Bayes, Logistic Regression, Neural Networks). Total bilirubin levels were categorized as <12.5 mg/dL (low-risk) and ≥12.5 mg/dL (high-risk). Models were evaluated using 10-fold cross-validation and performance metrics such as AUC, accuracy, and F1 score. Model interpretability was assessed using the Decrease in AUC method. Results: The Gradient Boosting model demonstrated the best performance on the test dataset with 92% accuracy, 0.90 F1 score, and an AUC of 0.89. While the models accurately predicted low-risk cases, their performance for high-risk (≥12.5 mg/dL) cases was limited, with error rates exceeding 90%. Feature importance analysis indicated that postnatal age (days), birth weight, and gestational age had the highest influence on predictions. Conclusion: ML models, especially Gradient Boosting, can effectively predict low-risk total bilirubin levels in neonates. However, to improve the identification of high-risk cases, approaches addressing class imbalance (e.g., SMOTE, cost-sensitive learning) should be considered.

Keywords

• Keywords: Machine learning
• neonatal jaundice
• total bilirubin
• Gradient Boosting
• decision support system

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