Parkinson's disease diagnosis by using autoencoder based on deep neural network (DNN) and metaheuristic method

Parkinson's disease (PD) is a neurodegenerative disorder and affects the nerve cells that produce dopamine in the brain. In this thesis, we investigated comparative studies on the different scenarios such as AutoEncoder and Ant Colony Optimization feature selection algorithms to determine the effective features in the diagnosis of Parkinson's disease. These algorithms are implemented to the voice obtained from an online repository. Then selected features are used with the Decision tree, SVM, K-NN, Ensemble, Naive Bayes and Discriminant classifiers for each of the binary classification problems. The proposed methods are evaluated with the sensitivity, specificity, precision, recall and accuracy criteria. The suggested systems are trained and tested with these classifiers separately to carry out a comparative study and to analyze the success of feature selection methods in discriminating healthy people and PD patients. In Parkinson's Disease Dataset 24 features were obtained from the signal voices. Some of the features in the training of the classifier have problems and these problems reduce the accuracy of the system. It is found that for K-NN and Ensemble classification methods both Ant Colony Optimization (ACO) and Autoencoder have the same and the best training performance. Testing results show that the accuracy rate of the improved ACO is higher than the Autoencoder method. Keywords: Improved ant colony optimization, autoencoder, deep learning, feature selection, parkinson's disease.