Early Spotting of Parkinson’s Illness Using Machine Learning Techniques

Abstract

Early Parkinson's disease (PD) diagnosis is essential for preventing the illness's development and providing patients with the opportunity to take disease-specific therapy. Speech signal processing is widely used and has gained attention recently. In this project, we carry out a comparative analysis for effective dysphonia (a speech disorder) and Parkinson’s disease spotting using Machine Learning methods. To distinguish between PD patients and normal individuals. We used Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LREG), and K-Nearest Neighbor (KNN) algorithms to demonstrate a robust detection process. According to experimental findings, the KNN outperformed other ML algorithms. The results of the trial conducted at UCI contain data from 31 patients, 23 of whom were diagnosed with PD. After implementing these algorithms, we found that our accuracy rates for these algorithms are NB (86.95%), RF (85.77%), SVM (79.18%), LREG (91.56%), DT (87.16%), and KNN (97.67%).