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Journals / Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi / 2026 / Cilt: 25 Sayı: 4

Decision Support Systems Based on Machine Learning for Detection of Liver Diseases

Karaciğer Hastalıklarının Tespiti İçin Makine Öğrenmesine Dayalı Karar Destek Sistemleri

Pınar İlter(İSTİNYE ÜNİVERSİTESİ)0009-0009-4014-8230
Yasin Kırelli(KÜTAHYA DUMLUPINAR ÜNİVERSİTESİ)0000-0002-3605-8621
Pages
827–837
Publication Date
2025-08-04
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AI Summary & Key Takeaways

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

Model results show that feature selection and normalization are essential in improving model accuracy.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The liver, the largest metabolic organ in the human body, plays a crucial role in maintaining essential functions.
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Key Findings & Data:However, the rise in liver diseases in recent years has become a significant public health issue.
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Conclusion & Impact:Model results show that feature selection and normalization are essential in improving model accuracy.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Karaciğer Hastalığı Tespiti · Makine Öğrenmesi · Sağlık Verisi

Abstract

The liver, the largest metabolic organ in the human body, plays a crucial role in maintaining essential functions. However, the rise in liver diseases in recent years has become a significant public health issue. There are various causes of these diseases, and the impact of technology in the health field has become necessary to analyze and manage diseases. Although there are various causes of liver diseases, analyzing and diagnosing them is crucial. Technological developments have made it possible to use machine learning methods to analyze large data sets. In this study, machine learning techniques are used in the early diagnosis and classification of liver disease, and the results are evaluated. The ‘ILPD (Indian Liver Patient Dataset)’ dataset in the UCI Machine Learning Repository has been evaluated using six modern machine learning techniques, including logistic regression, random forest, and decision trees. The study also analyses the effects of data preprocessing, feature selection, and normalization on model performance. Model results show that feature selection and normalization are essential in improving model accuracy. The Logistic Regression model achieved the best performance with an accuracy of 80%, while other algorithms, such as Random Forest and AdaBoost, also achieved high accuracy rates. This research demonstrates the potential of machine learning techniques in the early detection of liver diseases and provides a basis for future work.

Özet

İnsan vücudundaki en büyük metabolik organ olan karaciğer, yaşamsal işlevleri yerine getirmesi açısından kritik bir öneme sahiptir. Ancak, son yıllarda artan karaciğer hastalıkları toplum sağlığı üzerinde ciddi bir sorun haline gelmiştir. Bu hastalıkların çeşitli nedenleri bulunmakta olup teknolojinin sağlık alanındaki etkisi ile hastalıkların analizi ve yönetimi önemli hale gelmiştir. Karaciğer hastalıklarının çeşitli etkenleri bulunmakla birlikte, bu hastalıkların analizi ve teşhisi önemlidir. Teknolojik gelişmeler, büyük veri setlerinin analizinde makine öğrenimi yöntemlerini kullanmayı mümkün kılar hale getirmiştir. Bu çalışmada karaciğer hastalığı erken teşhisi ve sınıflandırmada makine öğrenmesi teknikleri kullanılmış ve sonuçları değerlendirilmiştir. UCI Machine Learning Repository'de bulunan "ILPD (Indian Liver Patient Dataset)" veri seti ile Lojistik Regresyon, Rastgele Orman, Karar Ağaçları gibi altı farklı modern makine öğrenmesi teknikleri ile değerlerndirilmiştir. Çalışma ek olarak veri ön işleme, özellik seçimi ve normalizasyonun model performansı üzerindeki etkilerini de analiz etmektedir. Model sonuçları özellik seçimi ve normalizasyonun model doğruluğunu artırmada önemli rol oynadığını göstermektedir. Lojistik Regresyon modeli %80 oranında doğruluk ile en iyi performans elde edilmiştir, Rastgele Orman ve AdaBoost gibi diğer algoritmalar da yüksek doğruluk oranlarına ulaşmıştır. Bu araştırma, karaciğer hastalıklarının erken teşhisinde makine öğrenmesi tekniklerinin potansiyelini ortaya koymakta ve gelecekteki çalışmalar için bir temel sunmaktadır.

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Cite This Article

İlter, P., Kırelli, Y. (2025). Decision Support Systems Based on Machine Learning for Detection of Liver Diseases. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, Cilt: 25 Sayı: 4, 827–837. https://doi.org/10.35414/akufemubid.1580440

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