Parkinson's disease prediction using machine learning approaches

Abstract

This paper proposes the application of a Fully Complex-Valued Radial Basis Function network (FC-RBF), Meta-Cognitive Fully Complex-Valued Radial Basis Function network (Mc-FCRBF) and Extreme Learning Machine (ELM) for the prediction of Parkinson's disease. With the help of Unified Parkinson's Disease Rating Scale (UPDRS), the severity of the Parkinson's disease is predicted and for untreated patients, the UPDRS scale spans the range (0-176). The FC-RBF network uses a fully complex valued activation function sech, which maps c <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> → c. The performance of the complex RBF network depends on the number of neurons and initialization of network parameters. The implementation of the self-regulatory learning mechanism in the FC-RBF network results in Mc-FCRBF network. It has two components: a cognitive component and a meta-cognitive component. The meta-cognitive component decides how to learn, what to learn and when to learn based on the knowledge acquired by the FC-RBF network. Extreme learning mechanism uses sigmoid activation function and it works with fast speed. In ELM network, the real valued inputs and targets are applied to the network. The result indicates that the Mc-FCRBF network has good prediction accuracy than ELM and FC-RBF network.