Parkinson’s Disease Identification using KNN and ANN Algorithms based on Voice Disorder


Because of its vast use, speech Signal Processing has received a lot of attention in recent years. We lead a comparative investigation for effective identification of Parkinson's disease using Machine Learning classifiers from a vocal issue called dysphonia in this paper. We employed Artificial Neural Networks (ANN) and K Nearest Neighbors (KNN) algorithms to prove a robust detection procedure for discriminating between PD patients and healthy individuals. In terms of accuracy, experimental data reveal that the ANN classifier outperformed the KNN classifier on average. The UCI Experiment had 31 participants, 23 of whom were diagnosed with Parkinson's disease. Using ANN, the established system can distinguish healthy persons from an acceptable range of people with PD with a 96.7 percent accuracy rate.

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