The paper presents a pilot research on the application of clinical decision support systems in a atrophic gastritis screening task. Two different DSS learning strategies have been tested - a standalone classifier and classifier ensemble application. Such classification algorithms as C4.5, CART, JRip and Naive Bayes were used as base classifiers. The classifiers were evaluated on the respondent medical data from an inquiry form, containing 28 attributes and 840 records. The dataset was preprocessed using simple methods in initial data analysis as well as more complex data mining methods for feature selection. The obtained results are summarized and discussed in order to summarize an information on what learning strategies are more applicable to the present dataset and should be studied in more detail in primary research.