Matches in SemOpenAlex for { <https://semopenalex.org/work/W4367320098> ?p ?o ?g. }
Showing items 1 to 87 of
87
with 100 items per page.
- W4367320098 endingPage "5449" @default.
- W4367320098 startingPage "5449" @default.
- W4367320098 abstract "Diabetic cardiovascular disease is a common complication of diabetes, which can lead to high-mortality diseases such as diabetic cardiomyopathy and atherosclerosis in serious cases. Therefore, effective prevention and management of diabetic cardiovascular disease is demanded. Clinical medical data officers are faced with a situation of a small amount of data and uneven data distribution. In this paper, we propose data oversampling synthesis techniques based on weight and extension algorithms. It can combine 1D-convolutional neural networks and long short-term memory neural networks to solve the problem of a lack of original data. First of all, a few samples based on feature weight are synthesized to make the original unbalanced data evenly distributed. Secondly, the original data are extended and corrected to expand the number of samples. Finally, the deep learning algorithm is used to extract features and classify whether the data have diabetic cardiovascular disease. Data synthesis based on weight and extension algorithms was evaluated on the actual medical datasets and obtained an accuracy of 93.53% and specificity of 94.37%, which confirms that it is an improved solution compared to the other algorithms. Hence, this paper contributes not only a substantial saving of human resources but also improves the efficiency of the clinical diagnosis of diabetic cardiovascular disease, which is conducive to the early detection and treatment of diseases." @default.
- W4367320098 created "2023-04-29" @default.
- W4367320098 creator A5025229356 @default.
- W4367320098 creator A5045160787 @default.
- W4367320098 creator A5064841402 @default.
- W4367320098 date "2023-04-27" @default.
- W4367320098 modified "2023-10-18" @default.
- W4367320098 title "Improvement of Auxiliary Diagnosis of Diabetic Cardiovascular Disease Based on Data Oversampling and Deep Learning" @default.
- W4367320098 cites W1989661643 @default.
- W4367320098 cites W2009787667 @default.
- W4367320098 cites W2014286416 @default.
- W4367320098 cites W2015452969 @default.
- W4367320098 cites W2028018231 @default.
- W4367320098 cites W2111364271 @default.
- W4367320098 cites W2116570678 @default.
- W4367320098 cites W2136848157 @default.
- W4367320098 cites W2148143831 @default.
- W4367320098 cites W2168261002 @default.
- W4367320098 cites W2185217707 @default.
- W4367320098 cites W2615770320 @default.
- W4367320098 cites W2766296277 @default.
- W4367320098 cites W2794210434 @default.
- W4367320098 cites W2889976627 @default.
- W4367320098 cites W3011884806 @default.
- W4367320098 cites W3089310265 @default.
- W4367320098 cites W4323663642 @default.
- W4367320098 doi "https://doi.org/10.3390/app13095449" @default.
- W4367320098 hasPublicationYear "2023" @default.
- W4367320098 type Work @default.
- W4367320098 citedByCount "0" @default.
- W4367320098 crossrefType "journal-article" @default.
- W4367320098 hasAuthorship W4367320098A5025229356 @default.
- W4367320098 hasAuthorship W4367320098A5045160787 @default.
- W4367320098 hasAuthorship W4367320098A5064841402 @default.
- W4367320098 hasBestOaLocation W43673200981 @default.
- W4367320098 hasConcept C119857082 @default.
- W4367320098 hasConcept C124101348 @default.
- W4367320098 hasConcept C126322002 @default.
- W4367320098 hasConcept C134018914 @default.
- W4367320098 hasConcept C154945302 @default.
- W4367320098 hasConcept C197323446 @default.
- W4367320098 hasConcept C199360897 @default.
- W4367320098 hasConcept C2776257435 @default.
- W4367320098 hasConcept C2778029271 @default.
- W4367320098 hasConcept C2779134260 @default.
- W4367320098 hasConcept C31258907 @default.
- W4367320098 hasConcept C41008148 @default.
- W4367320098 hasConcept C50644808 @default.
- W4367320098 hasConcept C555293320 @default.
- W4367320098 hasConcept C71924100 @default.
- W4367320098 hasConcept C81363708 @default.
- W4367320098 hasConceptScore W4367320098C119857082 @default.
- W4367320098 hasConceptScore W4367320098C124101348 @default.
- W4367320098 hasConceptScore W4367320098C126322002 @default.
- W4367320098 hasConceptScore W4367320098C134018914 @default.
- W4367320098 hasConceptScore W4367320098C154945302 @default.
- W4367320098 hasConceptScore W4367320098C197323446 @default.
- W4367320098 hasConceptScore W4367320098C199360897 @default.
- W4367320098 hasConceptScore W4367320098C2776257435 @default.
- W4367320098 hasConceptScore W4367320098C2778029271 @default.
- W4367320098 hasConceptScore W4367320098C2779134260 @default.
- W4367320098 hasConceptScore W4367320098C31258907 @default.
- W4367320098 hasConceptScore W4367320098C41008148 @default.
- W4367320098 hasConceptScore W4367320098C50644808 @default.
- W4367320098 hasConceptScore W4367320098C555293320 @default.
- W4367320098 hasConceptScore W4367320098C71924100 @default.
- W4367320098 hasConceptScore W4367320098C81363708 @default.
- W4367320098 hasIssue "9" @default.
- W4367320098 hasLocation W43673200981 @default.
- W4367320098 hasOpenAccess W4367320098 @default.
- W4367320098 hasPrimaryLocation W43673200981 @default.
- W4367320098 hasRelatedWork W1563850031 @default.
- W4367320098 hasRelatedWork W2415759662 @default.
- W4367320098 hasRelatedWork W2748952813 @default.
- W4367320098 hasRelatedWork W2899084033 @default.
- W4367320098 hasRelatedWork W2961085424 @default.
- W4367320098 hasRelatedWork W3021430260 @default.
- W4367320098 hasRelatedWork W3027997911 @default.
- W4367320098 hasRelatedWork W3036934084 @default.
- W4367320098 hasRelatedWork W4206962509 @default.
- W4367320098 hasRelatedWork W4287776258 @default.
- W4367320098 hasVolume "13" @default.
- W4367320098 isParatext "false" @default.
- W4367320098 isRetracted "false" @default.
- W4367320098 workType "article" @default.