Machine Learning Applied to Speech Recordings for Parkinson’s Disease Recognition

Parkinson’s disease is a common neurological condition that occurs when dopamine production in the brain decreases significantly due to the degeneration of neurons in an area called the substantia nigra. One of its characteristics is the slow and gradual onset of symptoms, which are varied and include tremors at rest, rigidity, and slow speech. Voice changes are very common among patients, so analysis of voice recordings could be a valuable tool for early diagnosis of the disease. This study proposes an approach that compares different Machine Learning models for the diagnosis of the disease through the use of vocal recordings of the vowel a made by both healthy and sick patients and the identification of the subset of the most significant features The experiments were conducted on a data set available on the UCI repository, which collects 756 different recordings. The results obtained are very encouraging, reaching an F-score of 95%, which demonstrates the effectiveness of the proposed approach.