Explainable Approach For Early Diagnosis of Parkinson’s Using Audio Tracks

Artificial intelligence and Machine Learning represent a fundamental role in the medical field today, even in the case of neurode-generative diseases such as Parkinson’s disease; particularly in non-invasive diagnostics. This work presents a machine learning-based approach for the early detection of Parkinson’s disease through voice recordings. Additionally, explainability techniques are applied to highlight key vocal features associated with the condition, supporting clinical interpretation and future integration in digital healthcare systems. The study aims to contribute to the timely diagnosis of Parkinson’s, providing an in-depth understanding of the disease to open up new opportunities for a more personalised and precise approach to therapies, representing a first step toward the future of predictive medicine.