| Yazarlar |
Doç. Dr. Kemal AKYOL
Kastamonu Üniversitesi, Türkiye |
| Özet |
| 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. |
| Anahtar Kelimeler |
| Deep neural networks | Growing and pruning | Machine learning | Parkinson’s disease |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale |
| Dergi Adı | CMES-COMPUTER MODELING IN ENGINEERING SCIENCES |
| Dergi ISSN | 1526-1492 |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Makale Dili | İngilizce |
| Basım Tarihi | 02-2020 |
| Cilt No | 122 |
| Sayı | 2 |
| Sayfalar | 619 / 632 |
| Doi Numarası | 10.32604/cmes.2020.07632 |
| Makale Linki | https://www.techscience.com/CMES/v122n2/38316 |
| Atıf Sayıları | |
| SCOPUS | 3 |
