Detection of Parkinson Disease Using Computational Intelligence Methods

2020-08-01T15:04:54Z (GMT) by Elcin Huseyn Babek Guirimov
Parkinson's disease is a neurodegenerative movement disorder that causes voice/speech, and behavioral impairments. As a dysfunctional disease, it can be detected by a set of specific symptoms of patients. Such symptoms include both voice/speech and/or physical behavior/movement characteristics. For better detection, both sets of characteristics are used in our research. In this study, as a diagnostic model, we use a system based on multiple-layer (deep) feed-forward neural networks. The networks are trained with Differential Evolution training algorithm using in parallel a pair of data sets (training and validation sets) to avoid overfitting and improve model’s generalization ability (performance on untrained data). The applied DE algorithm has allowed avoiding local minima of error function during the training. A third data set is used for testing trained network performance. According to the obtained results, this method demonstrated better results than other existing approaches.