Parkinson’s disease prediction using modified gauss-newton method in feed-forward neural network

Abstract

Parkinson’s disease (PD) is a brain disorder, characterized by the relapse of the nervous system that spreads gradually in the body. The symptom of PD includes a loss of body control (moderate movement, resting tremors, postural shakiness etc.). So, it is required to detect at an early stage. Machine learning (ML) deals with a variety of probabilistic methods to identify a pattern in a dataset. Therefore, the research is carried out to predict the PD using Multilayer Feed-Forward Neural Network. In Neural Network (NN), weight optimization performed at each layer that plays a major role in the prediction. First-order weight optimization techniques are slow in computation because they reduce the sum of square error using parameter updating in the steepest descent way. In proposed work, a modified recursive Gauss-Newton method is used to optimize the weights for speed up the performance of Feed-Forward NN. This approach is compared with widely used optimization techniques. The Proposed method found better than other techniques and performs fast in Apache Spark than R-Studio framework.