The basic technology that reinvents machines to personalize human experiences is Machine Learning (ML), a branch of Artificial Intelligence (AI) and a strong buzzword in today’s digital world. Despite its success, the most significant limitation of ML is the lack of transparency behind its behavior, which leaves users with a poor understanding of how it makes decisions, such it is the case for Deep Learning models. If the final user does not trust a model, he will not use it. This is especially true in medical diagnosis practice: physicians cannot simply use the predictions of the model but must trust the results it provides. This work focuses on the automatic early detection of Parkinson's disease (PD), whose impact on both the individual's quality of life and social well-being is constantly increasing with the aging of the population. To this end, we propose an explainable approach based on Genetic Programming, called Grammar Evolution (GE). This technique uses context-free grammar to describe the language of the programs to be generated and evolved. In this case, the generated programs are the explicit classification rules for the diagnosis of the subjects. The results of the experiments obtained on the publicly available HandPD data set show GE's high expressive power and performance comparable to those of several ML models that have been proposed in the literature.