setsci

International Congress on Human-Computer Interaction, Optimization and Robotic Applications

SET Technology

10.36287/setsci.4.5.030

847ce8a1-44ff-4444-bdff-d8fcb216c39d

An Efficient Medical Diagnosis Algorithm Based on a Hybrid Neural Network with a Variable Adaptive Momentum and PSO Algorithm

Hameed

Alaa Ali

Ajlouni

Naim

Özyavaş

Adem

Orman

Zeynep

Güneş

Ali

Istanbul University-Cerrahpaşa Istanbul Aydin University

12 10 2019

8 152 157

2019

SET Technology

CC BY 4.0

Neural Network has been used in a number of scientific fields including medical diagnosis. It is clear that classification is one of the main challenges in any medical diagnosis. However, the drawbacks of conventional neural network classifier are summarized as slow convergence and tendency to be trapped in local minima. The aim of this study is to present a hybrid Back-Propagation algorithm with a variable adaptive momentum (BPVAM) and particle swarm optimization (PSO) (BPVAM-PSO). The PSO is an efficient training algorithm as it does not require any complex calculations. The BPVAM variable momentum, in this case, will start with a high value, which increases the convergence rate; the momentum value will decrease as the error rate is decreased. The best weights of the BPVAM are passed to the PSO. The PSO uses the weights to determine the best set of parameters and as a result, the number of hidden neurons is reduced. This will improve performance further. To verify the efficiency of the BPVAM-PSO algorithm it will be used for the classification of a number of different medical datasets. The results of the proposed algorithm are compared against the performance of the conventional BP Neural Networks and BPVAM algorithm.

Neural Networks Particle Swarm Adaptive momentum Optimization Medical Diagnosis

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