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Journals / Türk Doğa ve Fen Dergisi / 2024 / Cilt: 13 Sayı: 4

Ensemble and Non-Ensemble Machine Learning-Based Classification of Liver Cirrhosis Stages

Topluluk ve Topluluk Olmayan Makine Öğrenmesine Dayalı Karaciğer Sirozu Evrelerinin Sınıflandırılması

Zeinab Mahdi Moumin(ÇANKIRI KARATEKİN ÜNİVERSİTESİ)0009-0003-8889-9160
İrem Nur Ecemiş(ÇANKIRI KARATEKİN ÜNİVERSİTESİ)0000-0001-9535-2209
Mustafa Karhan(ÇANKIRI KARATEKİN ÜNİVERSİTESİ)0000-0001-6747-8971
Pages
153–161
Publication Date
2024-12-30
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AI Summary & Key Takeaways

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

The random forest model achieved the highest score among other model with 97.4 % accuracy with a 10-fold Cross-validation method.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Cirrhosis is a chronic liver condition characterized by gradual scarring of the tissue in the liver, which then leads to one of the more serious health problems.
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Key Findings & Data:Early diagnosis and detection of this condition are critical to managing the patient's situation and planning his treatment.
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Conclusion & Impact:The random forest model achieved the highest score among other model with 97.4 % accuracy with a 10-fold Cross-validation method.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Karaciğer sirozu · Yapay zeka · Karşılıklı bilgi

Abstract

Cirrhosis is a chronic liver condition characterized by gradual scarring of the tissue in the liver, which then leads to one of the more serious health problems. Early diagnosis and detection of this condition are critical to managing the patient's situation and planning his treatment. Machine learning is a computer science field in which many complex issues have otherwise been successfully resolved, especially in medicine. This work focuses on constructing an artificial intelligence system, assisted by machine learning algorithms, to help professionals diagnose liver cirrhosis at its early stage. In this paper, four different models have been constructed with the aid of clinical parameters of patients and machine learning techniques: Random Forest, KNN, histogram-based Gradient Boosting, and Soft Voting. Two Feature selection methods (Chi-Square and mutual information) have been combined to select the most relevant features in the dataset. Then non-ensemble and ensemble methods are used to detect the condition. The random forest model achieved the highest score among other model with 97.4 % accuracy with a 10-fold Cross-validation method.

Özet

Siroz, karaciğerdeki dokunun kademeli olarak yaralanmasıyla karakterize kronik bir karaciğer rahatsızlığıdır. Bu rahatsızlık ilerleyen dönemde daha ciddi sağlık sorunlarına yol açar. Bu rahatsızlığın erken teşhisi ve tespiti, hastanın durumunu yönetmek ve tedavisini planlamak için kritik öneme sahiptir. Makine öğrenimi, özellikle tıpta birçok karmaşık sorunun başarıyla çözüldüğü bir bilgisayar bilimi alanıdır. Bu çalışma, profesyonellerin karaciğer sirozunu erken aşamada teşhis etmelerine yardımcı olmak için makine öğrenimi algoritmalarıyla desteklenen bir yapay zeka sistemi oluşturmaya odaklanmaktadır. Bu makalede, hastaların klinik parametreleri ve makine öğrenimi tekniklerinin yardımıyla dört farklı model oluşturulmuştur: Rastgele Orman, KNN, Histogram Tabanlı Gradyan Artırma ve Yumuşak Oylama. Veri kümesindeki en alakalı özellikleri seçmek için iki özellik seçme yöntemi (Chi-square ve karşılıklı bilgi) birleştirilmiştir. Ardından durumu tespit etmek için topluluk dışı ve topluluk yöntemleri kullanılmıştır. Rastgele orman modeli, 10 katlı çapraz doğrulama yöntemi ile %97,4 doğrulukla diğer modeller arasında en yüksek puanı elde etmiştir.

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Cite This Article

Moumin, Z. M., Ecemiş, İ. N., Karhan, M. (2024). Ensemble and Non-Ensemble Machine Learning-Based Classification of Liver Cirrhosis Stages. Türk Doğa ve Fen Dergisi, Cilt: 13 Sayı: 4, 153–161. https://doi.org/10.46810/tdfd.1553699

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