Hepatocellular Carcinoma Detection Using Machine Learning Techniques

Ioannis Angelis; Themis Exarchos

doi:10.1007/978-3-030-78775-2_4

Hepatocellular Carcinoma Detection Using Machine Learning Techniques

Adv Exp Med Biol. 2021:1338:21-29. doi: 10.1007/978-3-030-78775-2_4.

Authors

Ioannis Angelis¹, Themis Exarchos²

Affiliations

¹ Department of Informatics, Ionian University, Corfu, Greece.
² Department of Informatics, Ionian University, Corfu, Greece. exarchos@ionio.com.

PMID: 34973006
DOI: 10.1007/978-3-030-78775-2_4

Abstract

Hepatocellular carcinoma (HCC) is a form of primary cancer appearing in the liver. In this work used the hepatocellular carcinoma dataset from the UCI machine learning repository and tested different techniques for feature selection and classification. The following algorithms were used: decision trees, random forests, SVMs, k-NN classifiers, AdaBoost, and gradient boost. The best results were obtained using gradient boost with 84% accuracy and 93% precision. Finally, we deployed the model to a web application as a decision support system for clinicians.

Keywords: Classification; Hepatocellular carcinoma; Machine learning.

MeSH terms

Algorithms
Carcinoma, Hepatocellular* / diagnosis
Humans
Liver Neoplasms* / diagnosis
Machine Learning
Software