CLASSIFYING LIVER DISEASE WITH BOOSTING MACHINE LEARNING APPROACHES

Year 2025, Volume: 33 Issue: 2, 1882 - 1892, 22.08.2025

Ümit Yılmaz , Erol Özçekiç

https://doi.org/10.31796/ogummf.1591951

Abstract

Liver diseases pose a significant global health challenge due to their impact on metabolic function and the difficulty of early detection. Traditional diagnostic methods such as liver biopsy have limitations due to their invasive nature and high costs. This research examines the application of advanced machine learning techniques such as Gradient Boosting, AdaBoost, XGBoost and CatBoost for classification of liver diseases using a publicly available dataset of 1700 clinical records. Statistical analyses identified key predictors such as age, body mass index (BMI), lifestyle factors, and liver function tests, which were used to train and evaluate the models. The performance of the models was evaluated using metrics such as accuracy, precision, recall and AUC-ROC. The CatBoost model showed the highest performance with an accuracy of 93.82%, while also producing the most consistent results with precision (91.97%), recall (96.62%), F1 score (94.25%) and AUC-ROC (95.64%). These results highlight the potential of machine learning-based approaches to improve diagnostic accuracy and reduce reliance on invasive procedures. The proposed framework can contribute to improving patient outcomes and optimizing healthcare resources by providing a foundation for real-time clinical decision support systems.

Keywords

Liver disease , Machine learning , Diagnosis , Classification , Boosting

BOOSTING MAKİNE ÖĞRENME YAKLAŞIMLARI İLE KARACİĞER HASTALIKLARININ SINIFLANDIRILMASI

Abstract

Karaciğer hastalıkları, metabolik fonksiyonlar üzerindeki etkileri ve erken teşhis zorlukları nedeniyle önemli bir küresel sağlık sorunu oluşturmaktadır. Karaciğer biyopsisi gibi geleneksel tanı yöntemleri, invaziv yapıları ve yüksek maliyetleri nedeniyle sınırlamalar taşımaktadır. Bu araştırma, 1.700 klinik kayıttan oluşan kamuya açık bir veri kümesi kullanarak karaciğer hastalıklarının sınıflandırılması için Gradient Boosting, AdaBoost, XGBoost ve CatBoost gibi gelişmiş makine öğrenmesi tekniklerinin uygulanmasını incelemektedir. İstatistiksel analizler, modelleri eğitmek ve değerlendirmek için kullanılan yaş, vücut kitle endeksi, yaşam tarzı faktörleri ve karaciğer fonksiyon testleri gibi temel belirleyicileri ortaya koymuştur. Modellerin performansı, doğruluk, kesinlik, duyarlılık ve AUC-ROC gibi metrikler kullanılarak değerlendirilmiştir. CatBoost modeli, %93,82 doğruluk oranı ile en yüksek performansı göstermiş, aynı zamanda kesinlik (%91,97), duyarlılık (%96,62), F1 skoru (%94,25) ve AUC-ROC (%95,64) değerleriyle en istikrarlı sonuçları üretmiştir. Bu sonuçlar, makine öğrenmesi tabanlı yaklaşımların tanı doğruluğunu artırma ve invaziv prosedürlere olan bağımlılığı azaltma potansiyelini vurgulamaktadır. Önerilen çerçeve, gerçek zamanlı klinik karar destek sistemleri için bir temel oluşturarak hasta sonuçlarının iyileştirilmesine ve sağlık hizmetleri kaynaklarının optimizasyonuna katkı sağlayabilir.

Keywords

Karaciğer hastalığı , Makine öğrenmesi , Tanı , Sınıflandırma , Boosting

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