A new Parkinson detection system based on evolutionary fast learning networks and voice measurements

Abstract

Parkinson's disease (PD) is becoming the second most neurological syndrome of the central nervous system after Alzheimer's disease. It causes diverse symptoms which include bradykinesia (slowness of movement), voice impairments, rigidity, tremor, and poor balance. PD recognition system based on voice has founded a non-invasive alternative, but involves rather complex measurements or variables. Therefore an attention is required toward new approaches for better forecasting accuracy. In this paper, an optimal fast learning network (FLN) based on genetic algorithm (GA) was established as PD diagnosis system. FLN is a double-parallel feed-forward neural network structure, and based on GA for feature reduction and hyperparameter optimisation of the FLN, it was used as a predictive model. Finally, the conducted experiments on the Parkinson data of voice recordings over ten fold cross-validation show that proposed system is less complex and also achieved better average classification results with an accuracy of 97.47%. At the same time, it is effective in automatic identification of important vocal features. Moreover, the highest average degree of improved accuracy was (2.1%) compared with other familiar wrappers including support vector machine and K-nearest neighbours in the similar conditions.