A deep learning approach for classification and diagnosis of Parkinson's disease

Deep learning grabs a center attraction in industries, deep learning techniques are having great potential and recently these potentials are applied to healthcare problems, including computer-aided detection/diagnosis, disease prediction. Deep learning techniques are playing an important role in the classification and prediction of the diseases. The popularity of deep learning approaches is because of their ability to handle a large amount of data related to the patients with accuracy, reliability in a short span of time. However, the practitioners may take time in analyzing and generating the reports. In this paper, we have proposed a Deep Neural Network-based classification model for the classification of Parkinson's disease. Our proposed method is one such good example giving faster and more accurate results for the classification of Parkinson's disease patients with excellent accuracy of 94.87%. We have also compared the results with other existing approaches like linear discriminant analysis, support vector machine, K-nearest neighbor, decision tree, classification and regression trees, random forest, linear regression, logistic regression, multi-layer perceptron, and Naive Bayes.