Abstract
Evolutionary and fuzzy models have been used increasingly in many decision support, optimization, and control tasks. Oncological (cancer) data and are largely numerical in representation for analysis, however some are images or smears. The focus here is on the numerical nature of large samples. Lack of precise knowledge characterizes a lot of numerical samples for analysis in oncological decision making. The work here explores evolutionary and fuzzy models that are suitable for determining prognostic outcome.
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Odusanya, A.A., Odetayo, M.O., Petrovic, D., Naguib, R.N.G. (2001). The Use of Evolutionary and Fuzzy Models for Oncological Prognosis. In: John, R., Birkenhead, R. (eds) Developments in Soft Computing. Advances in Soft Computing, vol 9. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1829-1_25
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DOI: https://doi.org/10.1007/978-3-7908-1829-1_25
Publisher Name: Physica, Heidelberg
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