This paper proposes a machine learning approach dealing with genetic programming to build classifiers through logical rule induction. In this context, we define and test a set of mutation operators across from different clinical datasets to improve the performance of the proposal for each dataset. The use of genetic programming for rule induction has generated interesting results in machine learning problems. Hence, genetic programming represents a flexible and powerful evolutionary technique for automatic generation of classifiers. Since logical rules disclose knowledge from the analyzed data, we use such knowledge to interpret the results and filter the most important features from clinical data as a process of knowledge discovery. The ultimate goal of this proposal is to provide the experts in the data domain with prior knowledge (as a guide) about the structure of the data and the rules found for each class, especially to track dichotomies and inequality. The results reached by our proposal on the involved datasets have been very promising when used in classification tasks and compared with other methods.