Data mining methods are applied to a medical task that seeks for the information about the influence of Helicobacter Pylori on the gastric cancer risk increase by analysing the adverse factors of individual lifestyle. In the process of data preprocessing, the data are cleared of noise and other factors, reduced in dimensionality, as well as transformed for the task and cleared of non-informative attributes. Data classification using C4.5, CN2 and k-nearest neighbour algorithms is carried out to find relationships between the analysed attributes and the descriptive class attribute – Helicobacter Pylori presence that could have influence on the cancer development risk. Experimental analysis is carried out using the data of the Latvian-based project “Interdisciplinary Research Group for Early Cancer Detection and Cancer Prevention” database.
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