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Experimental comparison of machine learning approaches to medical domains: a case study of genotype influence on oral cancer development

Zervakis Michalis, Alessandro Passaro, V. Stalbovskaya, Flavio Baronti, Anna Maria Rossi, M. Blazantonakis, D. De Rossi, Valentina Maggini, M. Marcucci, Antonina Starita, R. Gonçalvez, Alessio Micheli

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URIhttp://purl.tuc.gr/dl/dias/013C067A-BA28-40AC-9073-7FBC97707FA7-
Identifierhttp://www.researchgate.net/profile/Valentintina_Maggini/publication/255660292_EXPERIMENTAL_COMPARISON_OF_MACHINE_LEARNING_APPROACHES_TO_MEDICAL_DOMAINS_A_CASE_STUDY_OF_GENOTYPE_INFLUENCE_ON_ORAL_CANCER_DEVELOPMENT/links/540712f00cf2bba34c1e8954.pdf-
Languageen-
Extent6 pagesen
TitleExperimental comparison of machine learning approaches to medical domains: a case study of genotype influence on oral cancer developmenten
CreatorZervakis Michalisen
CreatorΖερβακης Μιχαληςel
CreatorAlessandro Passaroen
CreatorV. Stalbovskayaen
CreatorFlavio Barontien
CreatorAnna Maria Rossien
CreatorM. Blazantonakisen
Creator D. De Rossien
CreatorValentina Magginien
CreatorM. Marcuccien
CreatorAntonina Staritaen
CreatorR. Gonçalvezen
CreatorAlessio Michelien
Content SummaryResearch in medical domains is facing new challenges as the available information increases in quantity and quality. In this context, Machine Learning methodologies can provide the right tools for data analysis, which can cope with recurring problems in medical research, such as the integration of clinical and genetic data. In this study we provide an experimental comparison of an heterogeneous subset of Machine Learning methods. For such a purpose, a representative dataset for medical analysis was chosen which regards Head and Neck Squamous Cell Carcinoma (HNSCC). HNSCC is a kind of oral cancer associated with smoking and alcohol drinking habits; however the individual risk could be modified by genetic polymorphisms of enzymes involved in the metabolism of tobacco carcinogens and in the DNA repair mechanisms. To study this relationship, the data set comprised demographic and life-style (age, gender, smoke and alcohol), and genetic data (the individual genotype of 11 polymorphic genes), with the information on 124 HNSCC patients and 231 healthy controls. Strengths and weaknesses of the different algorithms when applied to medical datasets, such as the one considered, will be analyzed, with particular attention to the issue of missing values.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-10-24-
Date of Publication2005-
SubjectClinical laboratory techniciansen
SubjectMedical laboratory techniciansen
Subjectmedical technologistsen
Subjectclinical laboratory techniciansen
Subjectmedical laboratory techniciansen
Bibliographic CitationF. Baronti, F. Colla, V. Maggini, A. Micheli, A. Passaro, A. M. Rossi, A.Starita, V. Bevilacqua, S. Cambò, L. Cariello, G. Mastronardi, E.M. Biganzoli, P. Boracchi, F.A. Cardillo, A. Starita, D. Caramella, A. Cilotti, F. Odoguardi, S.T. ChandraShekar, G.L. Varanasi, D. D’Alimonte, D. Lowe, I.T. Nabney, M. Sivaraksa, F. Ferreira, P. Maló, E. Ifeachor, R. Gonçalvez, S.R.I. Gabran, E.F. El-Saadany, M.M.A. Salama, C. Iacconi, A. Cilotti, C. Marini, M. Moretti, D. Mazzotta, F. Odoguardi, F.A. Cardillo, A. Starita, M. Kounelakis, M. Blazantonakis, M. Zervakis, X. Kotsiakis, N. Lama, P. Boracchi, E.M. Biganzoli, M. Marcucci, F. Verdini, T. Leo, M. Marcucci, F. Verdini, T. Leo, D. Karch, H. Dickhaus, A. Micheli, A. Starita, C. Duce, R. Solaro, M.R. Tiné, F. Odoguardi, A. Cilotti, C. Marini, M. Moretti, D. Mazzotta, A. Vaccaro, C. Iacconi, F.A .Cardillo, A. Starita, Z. Theodosiou, F. Raimondo, M.E. Garefalaki, G. Karayannopoulou, K. Lyroudia, I .Pitas, I. Kostopoulos, A. Tognetti, F. Lorussi, M. Tesconi, R. Bartalesi, G. Zupone, D. De Rossi, S. Van Huffel, D. Timmerman, E.O. Madu, V. Stalbovskaya, B. Hamadicharef, E.C. Ifeachor .(2006).Experimental comparison of machine learning approaches to medical domains: a case study of genotype influence on oral cancer development.Presented at European Conference on Emergent Aspects in Clinical Data Analysis .[online].Available: http://www.researchgate.net/profile/Valentintina_Maggini/publication/255660292_EXPERIMENTAL_COMPARISON_OF_MACHINE_LEARNING_APPROACHES_TO_MEDICAL_DOMAINS_A_CASE_STUDY_OF_GENOTYPE_INFLUENCE_ON_ORAL_CANCER_DEVELOPMENT/links/540712f00cf2bba34c1e8954.pdfen

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