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Journals / Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi / 2026 / Cilt: 38 Sayı: 2

Predicting Liver Disease Using Decision Tree Ensemble Methods

Karar Ağacı Topluluğu Yöntemleri Kullanılarak Karaciğer Hastalığının Tahmin Edilmesi

Fırat Orhanbulucu(İNÖNÜ ÜNİVERSİTESİ)0000-0003-4558-9667
İrem Acer(ERCİYES ÜNİVERSİTESİ)
Fatma Latifoğlu(Erciyes Üniversitesi)
Semra İçer(ERCİYES ÜNİVERSİTESİ)
Pages
261–267
Publication Date
2022-08-23
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The most successful classification result was obtained with the Light GBM algorithm as 98.8%, 98.1%, 99.4%, and 0.98%, respectively, in terms of accuracy, precision, recall, and kappa statistics using 10-fold cross-validation.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Damages that may occur in the liver, which has an important task for the human body, can cause fatal consequences.
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Key Findings & Data:In this context, Random Forest (RF), J48, AdaBoost, Gradient Boosting Classifiers (GBC), and Light Gradient Boosting Machine (Light GBM) algorithms from bagging and boosting models were used.
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Conclusion & Impact:The most successful classification result was obtained with the Light GBM algorithm as 98.8%, 98.1%, 99.4%, and 0.98%, respectively, in terms of accuracy, precision, recall, and kappa statistics using 10-fold cross-validation.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Karaciğer Hastalığı · Sınıflandırma · Topluluk öğrenme

Abstract

Damages that may occur in the liver, which has an important task for the human body, can cause fatal consequences. For this reason, early diagnosis of liver disease is important. In this study, liver disease was tried to be diagnosed by using Ensemble learning methods, depending on several clinical values obtained from liver patients and healthy blood donors. In this context, Random Forest (RF), J48, AdaBoost, Gradient Boosting Classifiers (GBC), and Light Gradient Boosting Machine (Light GBM) algorithms from bagging and boosting models were used. The most successful classification result was obtained with the Light GBM algorithm as 98.8%, 98.1%, 99.4%, and 0.98%, respectively, in terms of accuracy, precision, recall, and kappa statistics using 10-fold cross-validation.

Özet

İnsan vücudu için önemli bir görevi olan karaciğerde meydana gelebilecekhasarlar ölümcül sonuçlara neden olabilir. Bu nedenle karaciğer hastalığının erkenteşhisi önemlidir. Bu çalışmada, karaciğer hastalarından ve sağlıklı kanbağışçılarından elde edilen çeşitli klinik değerlere bağlı olarak topluluk öğrenmeyöntemleri kullanılarak karaciğer hastalığı tahmin edilmeye çalışılmıştır. Çalışmadakullanılan klinik değerler açık erişim olarak paylaşılan University of California,Irvine (UCI)’den elde edilmiştir. Yapılan çalışmada kullanılan veriler dengesizdağılım gösterdiği için sınıflandırma aşamasından önce Sentetik Azınlık ÖrneklemArttırma yöntemi (SMOTE) kullanılarak veriler dengeli hale getirilmiştir. Dengelihale getirilen veriler torbalama ve artırma modellerinden Random Forest (RF), J48,AdaBoost, Gradient Boosting Classifiers (GBC) ve Light Gradient Boosting Machine(Light GBM) algoritmaları kullanılarak sınıflandırılmıştır. Kullanılan algoritmalarsonucunda en başarılı sınıflandırma sonuçlarını 10 kat çapraz doğrulamakullanılarak Light GBM algoritması vermiştir. Sınıflandırma sonuçları, %98,8doğruluk, %98,1 kesinlik, %99,4 geri çağırma ve %0,98 kappa istatistik oranı olarakelde edilmiştir.

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Cite This Article

Orhanbulucu, F., Acer, İ., Latifoğlu, F., İçer, S. (2022). Predicting Liver Disease Using Decision Tree Ensemble Methods. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Fen Bilimleri Dergisi, Cilt: 38 Sayı: 2, 261–267. https://ajindex.com/articles/1266798

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