RideNN‐OptDRN: Heart disease detection using RideNN based feature fusion and optimized deep residual network

Summary Heart disease detection through early‐stage syndrome remains as a main confront in present world situation. If it is not detected appropriate time, then this turns out to be the major cause of death. Several existing heart disease detection techniques are developed with lower detection perfo...

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Veröffentlicht in:Concurrency and computation Jg. 34; H. 28
Hauptverfasser: Merugula, Suneetha, Padmaja, Buddi, Veerubommu, Ragavi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken, USA John Wiley & Sons, Inc 25.12.2022
Wiley Subscription Services, Inc
Schlagworte:
Algorithms
Cardiovascular disease
Classification
deep residual network
Heart
heart disease
Heart diseases
Hydrologic cycle
Neural networks
Optimization
Optimization algorithms
rider optimization algorithm
social optimization algorithm
water cycle algorithm
ISSN:1532-0626, 1532-0634
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Zusammenfassung:Summary Heart disease detection through early‐stage syndrome remains as a main confront in present world situation. If it is not detected appropriate time, then this turns out to be the major cause of death. Several existing heart disease detection techniques are developed with lower detection performance and therefore it is very significant to introduce a novel heart disease detection model that poses the potential to detect heart disease from input data. A novel detection approach named, social water cycle algorithm‐based deep residual network (SWCA‐based DRN) is proposed for classification of heart disease. The developed SWCA algorithm is a newly designed by the hybridization of social optimization algorithm and water cycle algorithm. Here, an input data is initially preprocessed and the feature fusion procedure is carried out RV coefficient enabled rider optimization algorithm‐based neural network. With the fused feature result, heart disease classification is performed utilizing a DRN classifier where training procedure of DRN is done by proposed optimization algorithm, named SWCA. Furthermore, developed SWCA‐enabled DRN technique outperformed different other present heart disease detection approaches and attained superior performance concerning the performance measures, like testing accuracy, sensitivity, and specificity with highest values of 0.941, 0.954, and 0.925.
Bibliographie:ObjectType-Article-1
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ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.7355