Author(s): Leite, Gabriel A. ; Azevedo, Beatriz Flamia ; Pacheco, Maria F. ; Fernandes, Florbela P. ; Pereira, Ana I.
Date: 2026
Persistent ID: http://hdl.handle.net/10198/35174
Origin: Biblioteca Digital do IPB
Subject(s): Hepatocellular carcinoma; Machine learning; Health diagnosis; Cancer; Ensemble methods; Feature selection
The prevalence of hepatocellular carcinoma is expected to continue increasing worldwide, and its difficulty in early detection highlights the need for advanced monitoring technologies. As the disease progresses, it has a serious impact on patients’ health, and in severe cases, liver transplantation becomes the only viable solution, reinforcing its importance as a global health problem. This study proposes the use of different artificial intelligence methods to compare and understand them related to liver disease. Well-known algorithms such as Random Forest and Multi-Layer Perceptron were tested, as well as ensemble methods that exploit different modeling structures. The results showed that AdaBoost, Random Forest, and Gradient Boosting performed best with Area Under the Curve of 0.89, 0.86, and 0.84 respectively. To analyze their influence on clinical results, the best-performing model was reapplied only to the non-biochemical features that compose the dataset. The results indicate that portal vein thrombosis, diabetes, and hypertension are the most influential variables, with contributions of 29.48%, 20.50%, and 16.60%, respectively.
