ensemble methods

Predicting Lymphoma Development by Exploiting Genetic Variants and Clinical Findings in a Machine Learning-Based Methodology With Ensemble Classifiers in a Cohort of Sjögren’s Syndrome Patients

Author(s): Clio Mavragani, Konstantina D. Kourou, Costas Papaloukas, Michalis Voulgarelis, Andreas Goules, Dimitrios I. Fotiadis, Themis Exarchos, Andrianos Nezos, Eleni I. Georga, Vasileios C. Pezoulas, Athanasios G. Tzioufas, Haralampos M. Moutsopoulos
Predicting Lymphoma Development by Exploiting Genetic Variants and Clinical Findings in a Machine Learning-Based Methodology With Ensemble Classifiers in a Cohort of Sjögren’s Syndrome Patients 800 534 IEEE Open Journal of Engineering in Medicine and Biology (OJEMB)

Lymphoma development constitutes one of the most serious clinico-pathological manifestations of patients with Sjögren’s Syndrome (SS). Over the last decades the risk for lymphomagenesis in SS patients has been studied…

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