Matches in SemOpenAlex for { <https://semopenalex.org/work/W4210417332> ?p ?o ?g. }
Showing items 1 to 78 of
78
with 100 items per page.
- W4210417332 abstract "Signal processing methods usually diagnose heart disease, and the diagnosis of this type of disease by signal processing sometimes encounters many difficulties. To reduce diagnostic problems, careful feature selection and training are needed to analyze these signals. In this study, an attempt has been made to combine machine learning skills, such as neural network learning, with the Harris Hawks Optimization method to diagnose heart disease. In this paper, the heart disease diagnosis is analyzed with the feature selection method. For feature selection, the Harris Hawks Optimization Algorithm based on a fitting neural network is used. First, the Harris Hawks Optimization algorithm was implemented on the data, and the sample features were randomly selected. Then the sample features are trained by a neural network, and the best features are selected. Results show that the proposed method's accuracy, sensitivity, and precision for diagnosing heart disease are 92.75%, 92.15%, and 95.69%, respectively. The proposed method has a lower error in diagnosing heart disease from MLP, SVM, RF, and AdaBoost." @default.
- W4210417332 created "2022-02-08" @default.
- W4210417332 creator A5008867585 @default.
- W4210417332 creator A5032216127 @default.
- W4210417332 creator A5055837838 @default.
- W4210417332 date "2021-12-03" @default.
- W4210417332 modified "2023-10-01" @default.
- W4210417332 title "Harris Hawks Optimization (HHO) Algorithm based on Artificial Neural Network for Heart Disease Diagnosis" @default.
- W4210417332 cites W2126667102 @default.
- W4210417332 cites W2919979744 @default.
- W4210417332 cites W2943847483 @default.
- W4210417332 cites W2969811881 @default.
- W4210417332 cites W3017008119 @default.
- W4210417332 cites W3033045889 @default.
- W4210417332 cites W3085238269 @default.
- W4210417332 cites W3087808340 @default.
- W4210417332 cites W3089327060 @default.
- W4210417332 cites W3131963690 @default.
- W4210417332 cites W3134429483 @default.
- W4210417332 cites W3145876438 @default.
- W4210417332 cites W3154896153 @default.
- W4210417332 cites W3185516026 @default.
- W4210417332 doi "https://doi.org/10.1109/icmnwc52512.2021.9688348" @default.
- W4210417332 hasPublicationYear "2021" @default.
- W4210417332 type Work @default.
- W4210417332 citedByCount "3" @default.
- W4210417332 countsByYear W42104173322022 @default.
- W4210417332 countsByYear W42104173322023 @default.
- W4210417332 crossrefType "proceedings-article" @default.
- W4210417332 hasAuthorship W4210417332A5008867585 @default.
- W4210417332 hasAuthorship W4210417332A5032216127 @default.
- W4210417332 hasAuthorship W4210417332A5055837838 @default.
- W4210417332 hasConcept C11413529 @default.
- W4210417332 hasConcept C119857082 @default.
- W4210417332 hasConcept C12267149 @default.
- W4210417332 hasConcept C138885662 @default.
- W4210417332 hasConcept C141404830 @default.
- W4210417332 hasConcept C142724271 @default.
- W4210417332 hasConcept C148483581 @default.
- W4210417332 hasConcept C153180895 @default.
- W4210417332 hasConcept C154945302 @default.
- W4210417332 hasConcept C2776401178 @default.
- W4210417332 hasConcept C2780074459 @default.
- W4210417332 hasConcept C41008148 @default.
- W4210417332 hasConcept C41895202 @default.
- W4210417332 hasConcept C50644808 @default.
- W4210417332 hasConcept C71924100 @default.
- W4210417332 hasConceptScore W4210417332C11413529 @default.
- W4210417332 hasConceptScore W4210417332C119857082 @default.
- W4210417332 hasConceptScore W4210417332C12267149 @default.
- W4210417332 hasConceptScore W4210417332C138885662 @default.
- W4210417332 hasConceptScore W4210417332C141404830 @default.
- W4210417332 hasConceptScore W4210417332C142724271 @default.
- W4210417332 hasConceptScore W4210417332C148483581 @default.
- W4210417332 hasConceptScore W4210417332C153180895 @default.
- W4210417332 hasConceptScore W4210417332C154945302 @default.
- W4210417332 hasConceptScore W4210417332C2776401178 @default.
- W4210417332 hasConceptScore W4210417332C2780074459 @default.
- W4210417332 hasConceptScore W4210417332C41008148 @default.
- W4210417332 hasConceptScore W4210417332C41895202 @default.
- W4210417332 hasConceptScore W4210417332C50644808 @default.
- W4210417332 hasConceptScore W4210417332C71924100 @default.
- W4210417332 hasLocation W42104173321 @default.
- W4210417332 hasOpenAccess W4210417332 @default.
- W4210417332 hasPrimaryLocation W42104173321 @default.
- W4210417332 hasRelatedWork W1996541855 @default.
- W4210417332 hasRelatedWork W2101819884 @default.
- W4210417332 hasRelatedWork W2340694410 @default.
- W4210417332 hasRelatedWork W2779605423 @default.
- W4210417332 hasRelatedWork W2911198546 @default.
- W4210417332 hasRelatedWork W3105251098 @default.
- W4210417332 hasRelatedWork W3193301557 @default.
- W4210417332 hasRelatedWork W4226197438 @default.
- W4210417332 hasRelatedWork W4312332763 @default.
- W4210417332 hasRelatedWork W2345184372 @default.
- W4210417332 isParatext "false" @default.
- W4210417332 isRetracted "false" @default.
- W4210417332 workType "article" @default.