Matches in SemOpenAlex for { <https://semopenalex.org/work/W2981230839> ?p ?o ?g. }
Showing items 1 to 88 of
88
with 100 items per page.
- W2981230839 endingPage "630" @default.
- W2981230839 startingPage "617" @default.
- W2981230839 abstract "Data mining is an appropriate way to discover information and hidden patterns in large amounts of data, where the hidden patterns cannot be easily discovered in normal ways. One of the most interesting applications of data mining is the discovery of diseases and disease patterns through investigating patients' records. Early diagnosis of diabetes can reduce the effects of this devastating disease. A common way to diagnose this disease is performing a blood test, which, despite its high precision, has some disadvantages such as: pain, cost, patient stress, lack of access to a laboratory, and so on. Diabetic patients’ information has hidden patterns, which can help you investigate the risk of diabetes in individuals, without performing any blood tests. Use of neural networks, as powerful data mining tools, is an appropriate method to discover hidden patterns in diabetic patients’ information. In this paper, in order to discover the hidden patterns and diagnose diabetes, a water wave optimization(WWO) algorithm; as a precise metaheuristic algorithm, was used along with a neural network to increase the precision of diabetes prediction. The results of our implementation in the MATLAB programming environment, using the dataset related to diabetes, indicated that the proposed method diagnosed diabetes at a precision of 94.73%,sensitivity of 94.20%, specificity of 93.34%, and accuracy of 95.46%, and was more sensitive than methods such as: support vector machines, artificial neural networks, and decision trees." @default.
- W2981230839 created "2019-10-25" @default.
- W2981230839 creator A5012285072 @default.
- W2981230839 creator A5051178591 @default.
- W2981230839 creator A5070539507 @default.
- W2981230839 date "2019-11-01" @default.
- W2981230839 modified "2023-09-24" @default.
- W2981230839 title "Prediction and Diagnosis of Diabetes Mellitus Using a Water Wave Optimization Algorithm" @default.
- W2981230839 cites W105539523 @default.
- W2981230839 cites W1525423638 @default.
- W2981230839 cites W1998560003 @default.
- W2981230839 cites W2032991527 @default.
- W2981230839 cites W2049031114 @default.
- W2981230839 cites W2066513278 @default.
- W2981230839 cites W2082152047 @default.
- W2981230839 cites W2122475441 @default.
- W2981230839 cites W2127339508 @default.
- W2981230839 cites W2262790950 @default.
- W2981230839 cites W2484206054 @default.
- W2981230839 cites W2547447314 @default.
- W2981230839 cites W2569150577 @default.
- W2981230839 cites W2581465409 @default.
- W2981230839 cites W2768890282 @default.
- W2981230839 cites W2783741365 @default.
- W2981230839 cites W57241405 @default.
- W2981230839 cites W807187018 @default.
- W2981230839 doi "https://doi.org/10.22044/jadm.2018.6446.1758" @default.
- W2981230839 hasPublicationYear "2019" @default.
- W2981230839 type Work @default.
- W2981230839 sameAs 2981230839 @default.
- W2981230839 citedByCount "0" @default.
- W2981230839 crossrefType "journal-article" @default.
- W2981230839 hasAuthorship W2981230839A5012285072 @default.
- W2981230839 hasAuthorship W2981230839A5051178591 @default.
- W2981230839 hasAuthorship W2981230839A5070539507 @default.
- W2981230839 hasConcept C11413529 @default.
- W2981230839 hasConcept C119857082 @default.
- W2981230839 hasConcept C124101348 @default.
- W2981230839 hasConcept C134018914 @default.
- W2981230839 hasConcept C154945302 @default.
- W2981230839 hasConcept C169258074 @default.
- W2981230839 hasConcept C41008148 @default.
- W2981230839 hasConcept C50644808 @default.
- W2981230839 hasConcept C555293320 @default.
- W2981230839 hasConcept C71924100 @default.
- W2981230839 hasConcept C84525736 @default.
- W2981230839 hasConceptScore W2981230839C11413529 @default.
- W2981230839 hasConceptScore W2981230839C119857082 @default.
- W2981230839 hasConceptScore W2981230839C124101348 @default.
- W2981230839 hasConceptScore W2981230839C134018914 @default.
- W2981230839 hasConceptScore W2981230839C154945302 @default.
- W2981230839 hasConceptScore W2981230839C169258074 @default.
- W2981230839 hasConceptScore W2981230839C41008148 @default.
- W2981230839 hasConceptScore W2981230839C50644808 @default.
- W2981230839 hasConceptScore W2981230839C555293320 @default.
- W2981230839 hasConceptScore W2981230839C71924100 @default.
- W2981230839 hasConceptScore W2981230839C84525736 @default.
- W2981230839 hasIssue "4" @default.
- W2981230839 hasLocation W29812308391 @default.
- W2981230839 hasOpenAccess W2981230839 @default.
- W2981230839 hasPrimaryLocation W29812308391 @default.
- W2981230839 hasRelatedWork W2024557183 @default.
- W2981230839 hasRelatedWork W2036190841 @default.
- W2981230839 hasRelatedWork W2067580698 @default.
- W2981230839 hasRelatedWork W2182408796 @default.
- W2981230839 hasRelatedWork W2232347650 @default.
- W2981230839 hasRelatedWork W2737140247 @default.
- W2981230839 hasRelatedWork W2786056106 @default.
- W2981230839 hasRelatedWork W2901779613 @default.
- W2981230839 hasRelatedWork W2912442537 @default.
- W2981230839 hasRelatedWork W2917183966 @default.
- W2981230839 hasRelatedWork W3016928241 @default.
- W2981230839 hasRelatedWork W3023265004 @default.
- W2981230839 hasRelatedWork W3042679019 @default.
- W2981230839 hasRelatedWork W3107429030 @default.
- W2981230839 hasRelatedWork W3112380905 @default.
- W2981230839 hasRelatedWork W3130651714 @default.
- W2981230839 hasRelatedWork W3132998456 @default.
- W2981230839 hasRelatedWork W3135225825 @default.
- W2981230839 hasRelatedWork W3180545632 @default.
- W2981230839 hasRelatedWork W3194470021 @default.
- W2981230839 hasVolume "7" @default.
- W2981230839 isParatext "false" @default.
- W2981230839 isRetracted "false" @default.
- W2981230839 magId "2981230839" @default.
- W2981230839 workType "article" @default.