Abstract
The proposed classification model for risk factor estimation makes semi-automatic data analysis based on advanced machine-learning methods. The objective is to provide intelligent computer-based support for medical diagnostics. The developed fuzzy boundary classification determines risk factors importance and adjusts the threshold. Experimental results are presented.
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Andreeva, P. (2008). Classification Model for Estimation of Risk Factors in Cardiological Diagnostics. In: Dochev, D., Pistore, M., Traverso, P. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2008. Lecture Notes in Computer Science(), vol 5253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85776-1_32
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DOI: https://doi.org/10.1007/978-3-540-85776-1_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85775-4
Online ISBN: 978-3-540-85776-1
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