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ISSN: 2090-4908

International Journal of Swarm Intelligence and Evolutionary Computation
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Research Article

Training of Multilayer Perceptrons with Improved Particle Swarm Optimization for the Heart Diseases Prediction

Kelwade JP1* and Salankar SS2

1Bapurao Deshmukh College of Engineering, RTM Nagpur University, India

2G. H. Raisoni College of Engineering, RTM Nagpur University, India

*Corresponding Author:
Kelwade JP
Bapurao Deshmukh College of Engineering
RTM Nagpur University, India
Tel: +917755903397
E-mail: [email protected]

Received date: May 02, 2017; Accepted date: May 19, 2017; Published date: May 26, 2017

Citation: Kelwade JP, Salankar SS (2017) Training of Multilayer Perceptrons with Improved Particle Swarm Optimization for the Heart Diseases Prediction. Int J Swarm Intel Evol Comput 6:156. doi:10.4172/2090-4908.1000156

Copyright: © 2017 Kelwade JP, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

The study of Heart rate variability is recently gained momentum for an estimation of heart health. This paper suggests a new approach for enhancement of the prediction accuracy of Multi-Layer Perceptrons (MLP) neural network using improved Particle Swarm Optimization (IPSO) technique. The IPSO computes the weights and biases of MLP for the more accurate prediction of the cardiac arrhythmia classes. This study for heart condition prediction involves selection of Three types of heart signals including Left Bundle Branch Block (LBBB), Normal Sinus Rhythm (NSR), Right Bundle Branch Block (RBBB) from MIT-BIH arrhythmia database, formation of heart rate time series, extraction of features from RR interval time series, implementation of training algorithm and prediction of arrhythmia classes. Several experiments on the proposed training method are carried out to superior the convergence ability of MLP. The experimental results gives comparably better evaluation over gradient based Back-Propagation (BP) learning algorithm.

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