Abstract
In recent years, a technique known as thermography has been again seriously considered as a complementary tool for the pre-diagnosis of breast cancer. In this paper, we explore the predictive value of thermographic atributes, from a database containing 98 cases of patients with suspicion of having breast cancer, using Bayesian networks. Each patient has corresponding results for different diagnostic tests: mammography, thermography and biopsy. Our results suggest that these atributes are not enough for producing good results in the pre-diagnosis of breast cancer. On the other hand, these models show unexpected interactions among the thermographical attributes, especially those directly related to the class variable.
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Maria Yaneli, AA., Nicandro, CR., Efrén, MM., Enrique, MDCM., Nancy, PC., Héctor Gabriel, AM. (2013). Assessment of Bayesian Network Classifiers as Tools for Discriminating Breast Cancer Pre-diagnosis Based on Three Diagnostic Methods. In: Batyrshin, I., González Mendoza, M. (eds) Advances in Artificial Intelligence. MICAI 2012. Lecture Notes in Computer Science(), vol 7629. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37807-2_36
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DOI: https://doi.org/10.1007/978-3-642-37807-2_36
Publisher Name: Springer, Berlin, Heidelberg
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