Open Access
ARTICLE
Growing and Pruning Based Deep Neural Networks Modeling for Effective Parkinson’s Disease Diagnosis
Kemal Akyol1, *
1 Kastamonu University, Kuzeykent Yerleşkesi, Kastamonu, 37100, Turkey.
* Corresponding Author: Kemal Akyol. Email: kakyol@kastamonu.edu.tr.
(This article belongs to this Special Issue: Data Science and Modeling in Biology, Health, and Medicine)
Computer Modeling in Engineering & Sciences 2020, 122(2), 619-632. https://doi.org/10.32604/cmes.2020.07632
Received 13 June 2019; Accepted 18 September 2019; Issue published 01 February 2020
Abstract
Parkinson’s disease is a serious disease that causes death. Recently, a new
dataset has been introduced on this disease. The aim of this study is to improve the
predictive performance of the model designed for Parkinson’s disease diagnosis. By and
large, original DNN models were designed by using specific or random number of
neurons and layers. This study analyzed the effects of parameters, i.e., neuron number
and activation function on the model performance based on growing and pruning
approach. In other words, this study addressed the optimum hidden layer and neuron
numbers and ideal activation and optimization functions in order to find out the best Deep
Neural Networks model. In this context of this study, several models were designed and
evaluated. The overall results revealed that the Deep Neural Networks were significantly
successful with 99.34% accuracy value on test data. Also, it presents the highest
prediction performance reported so far. Therefore, this study presents a model promising
with respect to more accurate Parkinson’s disease diagnosis.
Keywords
Cite This Article
Akyol, K. (2020). Growing and Pruning Based Deep Neural Networks Modeling for Effective Parkinson’s Disease Diagnosis.
CMES-Computer Modeling in Engineering & Sciences, 122(2), 619–632.