Enhance the Performance of Back Propagation Algorithm Using Proportional Conflict Redistribution Rule for the Diagnosis of Hypertension - Sumathi, B (International Journal of Computer Sciences and Engineering, 2018)
The objective of this study is to diagnose the patients with hypertension using Artificial Neural Network. Learning rate and momentum coefficient are the parameters used to construct network. The values of parameters are selected randomly and then the values are increased or decreases in every step iteratively. The topologies like Number of hidden layers, number of hidden nodes and the type of activation functions are also used to construct network. In order to improve the accuracy of the network, the result obtained by Back Propagation algorithms has been fused using Proportional Conflict Redistribution (PCR) rule. The output of the Back Propagation networks is considered as the primary diagnosis results and fused this result with Proportional Conflict Redistribution (PCR) rule to get the final results. Fusion method proposed in this paper is to enhance the target performance and reduce the uncertainty level. The experimental result shows that the fusion method produced higher accuracy and lower level of uncertainty.