Açık Akademik Arşiv Sistemi

Survival Classification in Heart Failure Patients by Neural Network-Based Crocodile and Egyptian Plover (CEP) Optimization Algorithm

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dc.contributor.authors Akalin, Fatma
dc.date.accessioned 2024-02-23T11:13:52Z
dc.date.available 2024-02-23T11:13:52Z
dc.date.issued 2023
dc.identifier.issn 2193-567X
dc.identifier.uri http://dx.doi.org/10.1007/s13369-023-08183
dc.identifier.uri https://hdl.handle.net/20.500.12619/101901
dc.description Bu yayının lisans anlaşması koşulları tam metin açık erişimine izin vermemektedir.
dc.description.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.
dc.language.iso English
dc.relation.isversionof 10.1007/s13369-023-08183
dc.subject PARTICLE SWARM OPTIMIZATION
dc.subject SYSTEM
dc.title Survival Classification in Heart Failure Patients by Neural Network-Based Crocodile and Egyptian Plover (CEP) Optimization Algorithm
dc.type Article; Early Access
dc.relation.journal ARAB J SCI ENG
dc.identifier.doi 10.1007/s13369-023-08183
dc.identifier.eissn 2191-4281
dc.contributor.author Akalin, F
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı


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