Classification of Thyroid Diseases Using Machine Learning and Bayesian Graph Algorithms. Issue 40 (2022)

• Record Type: Journal Article
• Authors: Mollica, Giuseppe; Francesconi, Daniela; Costante, Gabriele; Moretti, Sonia; Giannini, Riccardo; Puxeddu, Efisio; Valigi, Paolo
• Is Part Of: IFAC-PapersOnLine. Volume 55:Issue 40(2022)
• Page Start: 67
• Page End: 72
• Publication Date: 2022
• Subjects: Modeling of Biomedical Systems -- Identification of Biomedical Models -- Machine Learning -- Thyroid Cancer -- Immunophenotype; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2023.01.050
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Thyroid cancer is a type of disease that affects the thyroid gland. The primary diagnosis of thyroid tumors based on histopathological patterns can be ambiguous in some cases, so the use of machine learning techniques might improve the classification of thyroid diseases. Moreover, the lack of high sampled datasets makes the classification issue even more complex. This paper proposes a comparative evaluation of two classical machine learning techniques and one Bayesian network framework. We use Exploratory Data Analysis techniques and oversampling methods for data preprocessing and overfitting reduction. Results show that the use of Bayesian network frameworks can help in integrating prior expertise knowledge in the classification problem and build new hypotheses about features interaction.