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In this paper, we focus on applying feature selection in the context of secondary diagnoses prediction starting from medico-economic data sources. The results of the prediction is used as guidelines for encoding secondary diagnoses which is a sensitive task in the hospitals requiring a lot of attention in order to be achieved properly. We propose a practical approach to select stable and relevant features from imbalanced datasets. The stability of features is obtained through the convergence of several FS methods to a fair number of features without being impacted by the sampled dataset. The quality of featured shall be deducted from the quality prediction of machine learning algorithms on the selected features. We evaluate the proposed approach on the PMSI database of the CHIC-CM hospital. Our results are quite interesting and opening discussions for these specific health care data supports. [ABSTRACT FROM AUTHOR]