Medical data suffer from uncertainty and a lack of uniformisation, making them hard to use in medical software, especially for patient screening in clinical trials. In this PhD work, we propose to deal with these problems using supervised classification methods. We will focus on 3 properties of these data : imbalance, uncertainty and volumetry. We propose the MOCA-I algorithm to cope with this partial classification combinatorial problem, that uses a multi-objective local search algorithm. After having confirmed the benefits of multiobjectivization in this context, we calibrate MOCA-I and compare it to the best algorithms of the literature, on both real data sets and imbalanced data sets from literature. MOCA-I generates rule sets that are statistically better than models obtained by the best algorithmes of the literature. Moreover, the models generated by MOCA-I are between 2 to 6 times shorter. Regarding balanced data, we propose the MOCA algorithm, statistically equivalent to best algorithms of literature. Then, we analyze both theoretically and experimentally the behaviors of MOCA and MOCA-I depending on imbalance. In order to help the decision maker to choose a solution and reduce over-fitting, we propose and evaluate different methods to handle all the Pareto solutions generated by MOCA-I. Finally, we show how this work can be integrated into a software application.
