Particle Swarm Optimization Supported Artificial Neural Network in Detection of Parkinson’s Disease
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2016, Vol 18, Issue 5
Abstract
Abstract: Recent research works have focused on detection of Parkinson’s disease using several machine learning techniques. Accurate separation of normal persons in the subjects under consideration from the persons being affected by Parkinson’s disease is a challenging job. In the present work Neural Network (NN)has been trained using Particle Swarm Optimization (PSO employed to detect persons being affected with Parkinson’s disease. The initial weight vector to the input layer of the NN has been optimized gradually using the optimization techniques to enhance the performance of NN to a greater extent. The experimental results ofthe proposed method have been compared with a well-known Multilayer Perceptron Feed-Forward Network (MLP-FFN) and also with the NN. Performance measures like accuracy, precision, recall and F-measure have been used to compare the performances of the algorithms. The experimental results have revealed significantimprovement over the existing performances to detect Parkinson’s disease using PSO.
Authors and Affiliations
Sufal Haldar
Keywords
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