Deep Neural Network Associate Efficient Elephant Herding Optimization for Diagnosing Angiographic Disease Condition

Abstract

The significant intention of the research is to diagnosing the normal and abnormal situations of heart diseases through the Artificial Intelligence (AI) technique. The analysis anticipates employing various AI techniques, amid Deep Neural Network (DNN) performs superior over other methods. Investigating the methodology of DNN, it is evident that weights associated with neurons play a vital role, and changes in weights influence the result. The research aims to identify appropriate weights to associate with neurons, which is time-consuming and complicated through the trial-and-error process. The complication urges incorporating optimization techniques to identify appropriate weights to diagnose heart disease situations. The techniques involved in this process are an evolutionary strategy and swarm intelligence strategy. The result shows an Efficient Elephant Herding Optimization (EEHO) performs better in the three-test case database. EEHO configure weights employed in DNN unveils accuracy of 98.9% in Cleveland database, 98.8% in Hungarian database, and 97.1% in Switzerland database. In general, the result from the three-test case database exhibits proficient performance in most test-case measures.