Abstract
This article describes the fuzzy, mixed prototype/production-rule methodology employed for knowledge representation and reasoning in ONCOGAL, a system designed to aid in the diagnosis of leukosis. ONCOGAL’s diagnostic process involves successive goals set by an automaton structure defined within each prototype. Analysis of the problem of weighting and combining evidence, including the role of expert-engineer communication during the initial installation of static knowledge, has led to the use of a novel method of hypothesis evaluation based on the classification of clinical data in different significance levels referred to by a set of evaluation rules.
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Barreiro, A., Mira, J., Marín, R., Delgado, A.E., Couselo, J.M. (1990). Fuzzy Knowledge Representation for Leukemia Diagnosis in Children Oncology. In: Janko, W.H., Roubens, M., Zimmermann, HJ. (eds) Progress in Fuzzy Sets and Systems. Theory and Decision Library, vol 5. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-2019-4_3
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DOI: https://doi.org/10.1007/978-94-009-2019-4_3
Publisher Name: Springer, Dordrecht
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