Abstract:
Heart failure is an incurable disease and shows general symptoms. The presence of general symptoms contrary to specific indications makes early diagnosis difficult. This study aims to obtain clear outputs with 12 different features provided by the UCI public dataset in the research space where there are uncertainties regarding the diagnosis of heart failure. For this, a neural network-based Crocodile and Egyptian Plover (CEP) optimization algorithm has been developed. This algorithm is based on the phenomena of the Egyptian plover fed with food scraps from the crocodile's teeth, and it models mutual benefit. In the first stage of the model, the starting points of the Egyptian plovers are randomly assigned to the crocodile's mouth, and the eating amount of the Egyptian plovers is calculated with the determined parameters. Then, the proportion of plovers-specific traits in the entire population is determined for each iteration adaptively, and the local best and global best parameters are calculated using the cost function. With these processes continuing until the number of iterations is concluded, the proposed CEP algorithm converges to the global optimum without getting stuck to local optima. Finally, the artificial neural network that is capable of learning relationships and patterns between data and the CEP algorithm is used together to optimize success. To show the effectiveness of the proposed approach, its performance is compared with five other algorithms and five different datasets. In most cases, the near-optimal solutions obtained by this hybrid structure are better than the outputs obtained by similar algorithms.
