Machine Learning Models for Parkinson's Detection Using Features of Speech

This research provides analysis of biomedical voice measurements based dataset and final performance results of Support Vector Machine and Random Forest Classifier. The performance of the two algorithms is assessed using metrics such as precision, recall and accuracy. The overall accuracy is observed to be 91.83% for RF Classifier and 85.71% for SVM. It then delves into the role of machine learning in healthcare and its potential in aiding the early detection and monitoring of PD. Both of these machine learning techniques have emerged as promising tools for the early detection and prediction of PD. This paper presents a comprehensive review of the current state of research on Parkinson's disease detection using machine learning.