| Abstrakt: |
Parkinson's disease (PD) is a neurodegenerative disease in the central nervous system. Recently, more researches have been conducted in the determination of PD prediction which is really a challenging task. Due to the disorders in the central nervous system, the syndromes like off sleep, speech disorders, olfactory and autonomic dysfunction, sensory disorder symptoms will occur. The earliest diagnosing of PD is very challenging among the doctors community. There are techniques that are available in order to predict PD using symptoms and disorder measurement. It helps to save a million lives of future by early prediction. In this article, the early diagnosing of PD using machine learning techniques with feature selection is carried out. In the first stage, the data preprocessing is used for the preparation of Parkinson's disease data. In the second stage, MFEA is used for extracting features. In the third stage, the feature selection is performed using multiple feature input with a principal component analysis (PCA) algorithm. Finally, a Darknet Convolutional Neural Network (DNetCNN) is used to classify the PD patients. The main advantage of using PCA-DNetCNN is that, it provides the best classification in the image dataset using YOLO. In addition to that, the results of various existing methods are compared and the proposed DNetCNN proves better accuracy, performance in detecting the PD at the initial stages. DNetCNN achieves 97.5 % of accuracy in detecting PD as early. Besides, the other performance metrics are compared in the result evaluation and it is proved that the proposed model outperforms all the other existing models. [ABSTRACT FROM AUTHOR] |
