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dc.contributor.advisor Ye, Xin en_US
dc.contributor.author Wu, John
dc.date.accessioned 2019-05-09T21:39:06Z
dc.date.available 2019-05-09T21:39:06Z
dc.date.issued 2019-05-09
dc.date.submitted 2019-04-22
dc.identifier.uri http://hdl.handle.net/10211.3/209929
dc.description.abstract Parkinson’s Disease is a degenerative disorder of the central nervous system. Diagnosis is made based on patient history and physical exam findings. Since speech is affected in up to 90% of Parkinson’s Disease patients, it has drawn interest as a target symptom for diagnostic testing. Using a relatively large dataset of speech sample features compiled from Parkinson’s Disease patients and healthy individuals, a multilayer perceptron artificial neural network was trained to accurately classify between these two types of samples. By experimenting with the number of neurons in a single hidden layer network, a model was quickly found that could reach 0.937 accuracy. Using the single hidden layer results to inform a search for viable deep learning models, two hidden layer networks were found that could improve accuracy to 0.952. Finding high accuracy using a large sample size dataset indicates that multilayer perceptron is a viable method to aid in diagnosis of Parkinson’s Disease in the general population. This research demonstrates a potential search strategy for deep learning models that does not require the time and computational power of a comprehensive search of all possible models. The most accurate models found obtained 100% sensitivity. Thus, this method would be very useful as a screening test for Parkinson’s Disease as it is also non-invasive, quick, and can be done remotely. en_US
dc.description.sponsorship Computer Science en_US
dc.language.iso en_US en_US
dc.subject Neural Networks en_US
dc.subject Parkinson's Disease en_US
dc.subject Multilayer Perceptron en_US
dc.title Application of Artificial Neural Network on Speech Signal Features for Parkinson’s Disease Classification en_US
dc.genre Thesis en_US
dc.contributor.committeemember Yoshii, Rika en_US


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