Parkinson’s Disease Diagnosis: Towards Grammar-based Explainable Artificial Intelligence

Machine Learning (ML) approaches are vastly used for supporting humans in decision-making processes. However, the poor explainability associated to their behavior hampers their application in fields were the impact of the decision is critical, as it is the case for medical application, since physicians cannot simply use the predictions of the model but they must trust the results it provides. This work focuses on the automatic 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.