**Abstract**

Intestinal infections in common and colorectal cancer in particular are quite widely spread and affect modern population in a significant manner. Therefore, they have been objects of intensive scientific research for quite a long time. It is known that the colorectal cancer's diagnostics can face some difficulties caused by the uncertainties in patients' health status and disease data. The uncertainty, in common, can be classified as probabilistic or possibilistic (fuzzy). The goal of this chapter is to analyze a fuzzyrule-based medical expert system for the colorectal cancer's diagnostics. In the modeling, fuzzy inference based on possibility measure and knowledge extraction based on fuzzy clustering are applied. During the initial stage of the system's modeling, the applied parameters of colorectal cancer were defined by using clinical data. During the next stage, the soft-computing-based evaluation of the cancer's factors is performed. During the third stage, the applied fuzzy inference, based on possibility measure, is introduced and supported by the examples. The knowledge base of the modeled system consists of the case data obtained from 100 patients in the course of 3 years by the National Center of Oncology. The effectiveness of the modeled system was checked on the testing subset of 30 diagnoses, and 22 predictions by the expert system were defined as correct.

**Keywords:** colorectal cancer, IF-THEN rules, possibility measure-based inference system, tumor response, fuzzy logic, fuzzy inference, fuzzy clustering
