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A Case Study of Parkinson’s Disease Diagnosis using Artificial Neural Networks

by Farhad Soleimanian Gharehchopogh, Peyman Mohammadi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 73 - Number 19
Year of Publication: 2013
Authors: Farhad Soleimanian Gharehchopogh, Peyman Mohammadi
10.5120/12990-9206

Farhad Soleimanian Gharehchopogh, Peyman Mohammadi . A Case Study of Parkinson’s Disease Diagnosis using Artificial Neural Networks. International Journal of Computer Applications. 73, 19 ( July 2013), 1-6. DOI=10.5120/12990-9206

@article{ 10.5120/12990-9206,
author = { Farhad Soleimanian Gharehchopogh, Peyman Mohammadi },
title = { A Case Study of Parkinson’s Disease Diagnosis using Artificial Neural Networks },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 73 },
number = { 19 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume73/number19/12990-9206/ },
doi = { 10.5120/12990-9206 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:40:29.626392+05:30
%A Farhad Soleimanian Gharehchopogh
%A Peyman Mohammadi
%T A Case Study of Parkinson’s Disease Diagnosis using Artificial Neural Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 73
%N 19
%P 1-6
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Artificial Neural Network (ANN)-based diagnosis of medical diseases has been taken into great consideration in recent years. In this paper, two types of ANNs are used to classify effective diagnosis of Parkinson's disease. Multi-Layer Perceptron (MLP) with back-propagation learning algorithm and Radial Basis Function (RBF) ANNs were used to differentiate between clinical variables of samples (N = 195) who were suffering from Parkinson's disease and who were not. For this purpose, Parkinson's disease data set, taken from UCI machine learning database was used. Mean squared normalized error function was used to measure the usefulness of our networks during trainings and direct performance calculations. It was observed that MLP is the best classi?cation with 93. 22% accuracy for the data set. Also, we got 86. 44% accuracy in RBF classification for the same data set. This technique can assist neurologists to make their ultimate decisions without hesitation and more astutely.

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Index Terms

Computer Science
Information Sciences

Keywords

Parkinson's disease Artificial Neural Network Multi-layer Perceptron Radial Basis Function

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