Abstract
Melanoma is one of the most important cancers to study in our current social context. This kind of cancer has increased its frequency in the last few years and its mortality is around twenty percent if it is not early treated. In order to improve the early diagnosis, the problem characterization using Machine Learning (ML) is crucial to identify melanoma patterns. Therefore we need to organize the data so that we can apply ML on it. This paper presents a detailed characterization based on the most relevant knowledge in melanomas problem and how to relate them to apply Data Mining techniques to aid medical diagnosis in melanoma and improve the research in this field.
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Nicolas, R., Golobardes, E., Fornells, A., Puig, S., Carrera, C., Malvehy, J. (2009). Identification of Relevant Knowledge for Characterizing the Melanoma Domain. In: Corchado, J.M., De Paz, J.F., Rocha, M.P., Fernández Riverola, F. (eds) 2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008). Advances in Soft Computing, vol 49. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85861-4_7
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DOI: https://doi.org/10.1007/978-3-540-85861-4_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85860-7
Online ISBN: 978-3-540-85861-4
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