The demand for transparent and explainable AI solutions in healthcare has highlighted the limitations of purely data-driven models and emphasized the value of symbolic methods such as Fuzzy Inference Systems, which provide understandable rules and facilitate collaboration with human experts. To overcome the limitations and merge the strenghts of individual approaches, hybrid models that integrate sub-symbolic and symbolic reasoning have emerged as a promising direction. For example, Fuzzy Neural Networks merge the predictive strength of neural networks with the clarity of fuzzy systems, and their performance can be further enhanced through evolutionary techniques that optimize parameters and improve adaptability. In this study, we propose an evolutionary Fuzzy Neural Network framework that use a genetic algorithm to strengthen classification capabilities while maintaining interpretability. By incorporating the evolutionary optimization into the network’s parameter update process, the model achieves both robustness and transparency. Validation on the Maternal Health Risk dataset demonstrates that the proposed approach effectively balances predictive accuracy with explainability.