Hybrid PSO-RBFNN and Proposed Algorithms of DDDWT for the Heart Disease Classification

This paper introduced a Particle Swarm Optimization-Radial Basis Function Neural Networks (PSO-RBFNN)-based system for heart disease detection that used the PSO algorithm to optimize RBFNN parameters. The newly developed signal digital presents results of new image contrast enhancement approach using Double Density Discrete Wavelet transform DDDWT extraction features, adaptive elimination noise, and use ANN methods classify output from Electrocardiogram (EGGS). It also provides identification all techniques MATLAB codes improve processes. merged global search power with high efficiency RBFNN's local optimums, overcome inconsistency downside, quickly leading minimum. show that, as compared other approaches, PSO-RBFNN model diagnosis is highly accurate in detecting predicting.