Machine Learning Ensemble Algorithms for Classification of Thyroid Nodules Through Proteomics: Extending the Method of Shapley Values from Binary to Multi-Class Tasks

Giulia Capitoli (), Simone Magnaghi, Andrea D'Amicis, Camilla Vittoria Di Martino, Isabella Piga, Vincenzo L'Imperio, Marco Salvatore Nobile, Stefania Galimberti and Davide Paolo Bernasconi
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Giulia Capitoli: Bicocca Bioinformatics Biostatistics and Bioimaging B4 Center, Department of Medicine and Surgery, University of Milano–Bicocca, 20900 Monza, Italy
Simone Magnaghi: Department of Informatics, Systems, and Communication, University of Milano–Bicocca, 20126 Milan, Italy
Andrea D'Amicis: Department of Informatics, Systems, and Communication, University of Milano–Bicocca, 20126 Milan, Italy
Camilla Vittoria Di Martino: Department of Informatics, Systems, and Communication, University of Milano–Bicocca, 20126 Milan, Italy
Isabella Piga: Proteomics and Metabolomics Unit, Department of Medicine and Surgery, University of Milano–Bicocca, 20900 Monza, Italy
Vincenzo L'Imperio: Pathology Unit, Department of Medicine and Surgery, Fondazione IRCCS San Gerardo dei Tintori, University of Milano–Bicocca, 20900 Monza, Italy
Marco Salvatore Nobile: Department of Environmental Sciences, Informatics and Statistics, Ca’ Foscari University of Venice, 30100 Venice, Italy
Stefania Galimberti: Bicocca Bioinformatics Biostatistics and Bioimaging B4 Center, Department of Medicine and Surgery, University of Milano–Bicocca, 20900 Monza, Italy
Davide Paolo Bernasconi: Bicocca Bioinformatics Biostatistics and Bioimaging B4 Center, Department of Medicine and Surgery, University of Milano–Bicocca, 20900 Monza, Italy

Stats, 2025, vol. 8, issue 3, 1-17

Abstract: The need to improve medical diagnosis is of utmost importance in medical research, consisting of the optimization of accurate classification models able to assist clinical decisions. To minimize the errors that can be caused by using a single classifier, the voting ensemble technique can be used, combining the classification results of different classifiers to improve the final classification performance. This paper aims to compare the existing voting ensemble techniques with a new game-theory-derived approach based on Shapley values. We extended this method, originally developed for binary tasks, to the multi-class setting in order to capture complementary information provided by different classifiers. In heterogeneous clinical scenarios such as thyroid nodule diagnosis, where distinct models may be better suited to identify specific subtypes (e.g., benign, malignant, or inflammatory lesions), ensemble strategies capable of leveraging these strengths are particularly valuable. The motivating application focuses on the classification of thyroid cancer nodules whose cytopathological clinical diagnosis is typically characterized by a high number of false positive cases that may result in unnecessary thyroidectomy. We apply and compare the performance of seven individual classifiers, along with four ensemble voting techniques (including Shapley values), in a real-world study focused on classifying thyroid cancer nodules using proteomic features obtained through mass spectrometry. Our results indicate a slight improvement in the classification accuracy for ensemble systems compared to the performance of single classifiers. Although the Shapley value-based voting method remains comparable to the other voting methods, we envision this new ensemble approach could be effective in improving the performance of single classifiers in further applications, especially when complementary algorithms are considered in the ensemble. The application of these techniques can lead to the development of new tools to assist clinicians in diagnosing thyroid cancer using proteomic features derived from mass spectrometry.

Keywords: Shapley values; ensemble learning; multinomial classification problem; thyroid cancer; mass spectrometry (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2025
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