In many knowledge discovery experiments feature selection is obvious initial part. In the paper, some attempt to tree-based generational feature selection applications in medical data analysis is presented. This approach devotes to application of classification tree algorithm to estimate importance of attributes extracted from structure of the tree with recursive application of generational feature selection. This method apply removing of selected features from dataset and then creates next generation of important feature set. The process goes until the most important feature will be a random value. Implemented method were applied on three artificial and real-world medical datasets and the results of selection and classification are presented. They were mostly more efficient after selection than using original datasets.