Parkinson‟s Disease Prediction using Modified Gauss-Newton Method in Feed-Forward Neural Network
Madhuri Gupta1, Bharat Gupta2

1Madhuri Gupta*, Computer Engineering and Information Technology, ABES Engineering College, Ghaziabad, India.
2Bharat Gupta, Computer Science and Information Technology, Jaypee Institute of Information Technology, Noida, India.

Manuscript received on October 14, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 4986-4993 | Volume-9 Issue-1, November 2019. | Retrieval Number: K15590981119/2019©BEIESP | DOI: 10.35940/ijitee.A3912.119119
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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.
Keywords: Apache Spark, Feed-Forward Neural Network, Gauss-Newton Method, Gradient Descent, Machine learning, Parkinson’s Disease.
Scope of the Article: Machine learning