Abstract:
Aside from its effects on the nervous system, Parkinson's disease also modifies the
physiological functions that the nervous system regulates. The progressive nature of
Parkinson's disease makes it a degenerative disorder. A variety of symptoms, such as
rigidity, tremor, speech impediment, sluggishness, and trouble walking, can be
intentionally induced. These same principles are applicable to issues like depression,
anxiety disorders, and similar ailments. We aim to use machine learning algorithms to
identify cases of Parkinson's disease (PD) in patient-created audio recordings. Time
Frequency Features, Mel Frequency Cepstral Coefficients (MFCCs), Vocal Fold
Features, TWQT features, and Wavelet Transform-based Features are some of the
speech signal processing methods that were used to extract clinically important data
from the audio recordings in order to evaluate PD. In order to glean useful information
from the recordings, these methods were employed. The objective of this research
endeavour is to assess a number of these models by means of a number of machine
learning techniques, such as Logistic Regression, XGBoost, Adaboost, Decision Tree,
Support Vector Machine, Naive Bayes, and Random Forest, among others. The
application uses voice attribute-based data preparation, which allows for the
measurement of effectiveness. By the end of the experimental evaluation, the XGBoost
classifier had achieved the maximum achievable accuracy of 88%. The authors of this
study used explainable AI to zero in on the problem of creating models that laypeople
might understand.
