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

Authors : Zeinab Mahdi Moumin, İrem Nur Ecemiş, Mustafa Karhan

Pages : 153-161

Doi:10.46810/tdfd.1553699

View : 78 | Download : 69

Publication Date : 2024-12-30

Article Type : Research Paper

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.
Keywords : Karaciğer sirozu, Yapay zeka, Karşılıklı bilgi, Yumuşak oylama, K-katlı çapraz doğrulama

ORIGINAL ARTICLE URL