@Article{cmes.2020.07632,
AUTHOR = {Kemal Akyol},
TITLE = {Growing and Pruning Based Deep Neural Networks Modeling for Effective Parkinson’s Disease Diagnosis},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {122},
YEAR = {2020},
NUMBER = {2},
PAGES = {619--632},
URL = {http://www.techscience.com/CMES/v122n2/38324},
ISSN = {1526-1506},
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.},
DOI = {10.32604/cmes.2020.07632}
}