Matches in SemOpenAlex for { <https://semopenalex.org/work/W2501622818> ?p ?o ?g. }
Showing items 1 to 87 of
87
with 100 items per page.
- W2501622818 abstract "This paper discusses the use of various type of neural network architectures for the classification of medical data. Extensive research has indicated that neural networks generate significant improvements when used for the pre-processing of medical time-series data signals and have assisted in obtaining high accuracy in the classification of medical data. Up to date, most of hospitals and healthcare sectors in the United Kingdom are using manual approach for analysing patient input for sickle cell disease, which depends on clinician’s experience that can lead to time consuming and stress to patients. The results obtained from a range of models during our experiments have shown that the proposed Back-propagation trained feed-forward neural network classifier generated significantly better outcomes over the other range of classifiers. Using the Receiver Operating Characteristic curve, experiments results showed the following outcomes for our models, in order of best to worst: Back-propagation trained feed-forward neural net classifier: 0.989, Functional Link Neural Network: 0.972, in comparison to the Radial basis neural Network Classifiers with areas of 0.875, and the Voted Perception classifier: 0.766. A Linear Neural Network was used as baseline classifier to illustrate the importance of the previous models, producing an area of 0.849, followed by a random guessing model with an area of 0.524." @default.
- W2501622818 created "2016-08-23" @default.
- W2501622818 creator A5001007405 @default.
- W2501622818 creator A5015488050 @default.
- W2501622818 creator A5015808667 @default.
- W2501622818 creator A5025789067 @default.
- W2501622818 creator A5053123637 @default.
- W2501622818 creator A5058529910 @default.
- W2501622818 creator A5063707849 @default.
- W2501622818 creator A5078720792 @default.
- W2501622818 creator A5083103834 @default.
- W2501622818 date "2016-01-01" @default.
- W2501622818 modified "2023-09-29" @default.
- W2501622818 title "Training Neural Networks as Experimental Models: Classifying Biomedical Datasets for Sickle Cell Disease" @default.
- W2501622818 cites W1505135344 @default.
- W2501622818 cites W1594005194 @default.
- W2501622818 cites W1967354181 @default.
- W2501622818 cites W2017838688 @default.
- W2501622818 cites W2022261179 @default.
- W2501622818 cites W2038968264 @default.
- W2501622818 cites W2070849598 @default.
- W2501622818 cites W2072372077 @default.
- W2501622818 cites W2295044217 @default.
- W2501622818 cites W2492967162 @default.
- W2501622818 cites W1985670707 @default.
- W2501622818 doi "https://doi.org/10.1007/978-3-319-42291-6_78" @default.
- W2501622818 hasPublicationYear "2016" @default.
- W2501622818 type Work @default.
- W2501622818 sameAs 2501622818 @default.
- W2501622818 citedByCount "9" @default.
- W2501622818 countsByYear W25016228182017 @default.
- W2501622818 countsByYear W25016228182018 @default.
- W2501622818 countsByYear W25016228182019 @default.
- W2501622818 countsByYear W25016228182020 @default.
- W2501622818 crossrefType "book-chapter" @default.
- W2501622818 hasAuthorship W2501622818A5001007405 @default.
- W2501622818 hasAuthorship W2501622818A5015488050 @default.
- W2501622818 hasAuthorship W2501622818A5015808667 @default.
- W2501622818 hasAuthorship W2501622818A5025789067 @default.
- W2501622818 hasAuthorship W2501622818A5053123637 @default.
- W2501622818 hasAuthorship W2501622818A5058529910 @default.
- W2501622818 hasAuthorship W2501622818A5063707849 @default.
- W2501622818 hasAuthorship W2501622818A5078720792 @default.
- W2501622818 hasAuthorship W2501622818A5083103834 @default.
- W2501622818 hasBestOaLocation W25016228182 @default.
- W2501622818 hasConcept C119857082 @default.
- W2501622818 hasConcept C153180895 @default.
- W2501622818 hasConcept C154945302 @default.
- W2501622818 hasConcept C155032097 @default.
- W2501622818 hasConcept C41008148 @default.
- W2501622818 hasConcept C50644808 @default.
- W2501622818 hasConcept C95623464 @default.
- W2501622818 hasConceptScore W2501622818C119857082 @default.
- W2501622818 hasConceptScore W2501622818C153180895 @default.
- W2501622818 hasConceptScore W2501622818C154945302 @default.
- W2501622818 hasConceptScore W2501622818C155032097 @default.
- W2501622818 hasConceptScore W2501622818C41008148 @default.
- W2501622818 hasConceptScore W2501622818C50644808 @default.
- W2501622818 hasConceptScore W2501622818C95623464 @default.
- W2501622818 hasLocation W25016228181 @default.
- W2501622818 hasLocation W25016228182 @default.
- W2501622818 hasOpenAccess W2501622818 @default.
- W2501622818 hasPrimaryLocation W25016228181 @default.
- W2501622818 hasRelatedWork W160982082 @default.
- W2501622818 hasRelatedWork W1612417229 @default.
- W2501622818 hasRelatedWork W1974330405 @default.
- W2501622818 hasRelatedWork W1985009090 @default.
- W2501622818 hasRelatedWork W2002964621 @default.
- W2501622818 hasRelatedWork W2018513454 @default.
- W2501622818 hasRelatedWork W2019798812 @default.
- W2501622818 hasRelatedWork W2169773724 @default.
- W2501622818 hasRelatedWork W2200452948 @default.
- W2501622818 hasRelatedWork W2735786457 @default.
- W2501622818 hasRelatedWork W2914828093 @default.
- W2501622818 hasRelatedWork W2936365755 @default.
- W2501622818 hasRelatedWork W2951559695 @default.
- W2501622818 hasRelatedWork W2970374092 @default.
- W2501622818 hasRelatedWork W3091883770 @default.
- W2501622818 hasRelatedWork W3104270510 @default.
- W2501622818 hasRelatedWork W3109015346 @default.
- W2501622818 hasRelatedWork W3158181349 @default.
- W2501622818 hasRelatedWork W3182832259 @default.
- W2501622818 hasRelatedWork W3194703500 @default.
- W2501622818 isParatext "false" @default.
- W2501622818 isRetracted "false" @default.
- W2501622818 magId "2501622818" @default.
- W2501622818 workType "book-chapter" @default.