Description

Parkinson’s disease (PD) is the second most common long-term chronic, progressive, neurodegenerative disease, affecting more than 10 million people worldwide. This process is mainly aimed to utilize pre-trained classification structures and conventional machine learning (ML) models as an semi-automated approach to diagnose PD from voice signals. Primarily, we stacked the extracted features from three deep pre-trained architectures, including MFCC, to generate the discriminative feature vectors. Then the feature vectors are classified by means of the machine learning algorithm. The Result of the process is evaluated by means of Accuracy, sensitivity, specificity, precision and recall.