Novel fuzzy rule-based system (FRBS) for medical data classification tasks is proposed. FRBSs are designed using multi-objective evolutionary optimization algorithms. Set of FRBSs with various levels of accuracy-interpretability trade-off is generated. Benchmark medical data sets are used to evaluate the effectiveness of the system. Highly interpretable and accurate medical decision support is provided by our approach.