Parkinson’s Disease Identification using KNN and ANN Algorithms based on Voice Disorder
Asmae Ouhmida, Abdelhadi Raihani, Bouchaib Cherradi, Sandabad Sara, Tajeddine Khalili
Abstract
In recent years, speech signal processing has benefited from a lot of attention, because of its widespread application. In this study, we have led a comparative analysis for efficient detection of Parkinson's disease applied to machine learning classifiers from voice disorder known as dysphonia. To prove robust detection process, we used Artificial Neural Networks (ANN) and K Nearest Neighbors (KNN) algorithms, in the purpose of distinguishing between PD patient and healthy individual. Experimental results show that the ANN classifier achieved higher average performance than the KNN classifier in term of accuracy. The UCI Experiment consists of 31 subjects of which 23 were diagnosed with Parkinson's disease. The established system is able to distinguish healthy people from an acceptable range of people with PD with an accuracy rate of 96.7% by using ANN.