Predicting Trends in Retinol and Beta-Carotene Plasma Levels Using Neural Networks

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dc.contributor.authorKaikhah, Khosrow
dc.date.accessioned2012-02-24T10:17:52Z
dc.date.available2012-02-24T10:17:52Z
dc.date.issued2004-07-01
dc.description.abstractA novel knowledge discovery and prediction technique using neural networks is presented. A neural network is trained to learn the correlations among physical and dietary characteristics of several hundred people to their Retinol and Beta-Carotene Plasma Levels. The neural network is then pruned and modified to generalize the correlations and relationships existing in data. Finally, the neural network is used as a tool to discover and predict the hidden trends inherent in dataset.
dc.description.departmentComputer Science
dc.formatText
dc.format.extent6 pages
dc.format.medium1 file (.pdf)
dc.identifier.citationKaikhah, K. (2004). Predicting Trends in Retinol and Beta-Carotene Plasma Levels using Neural Networks. WSEAS Transactions On Information Science and Applications, 1(1), pp. 547-552.
dc.identifier.urihttps://hdl.handle.net/10877/3816
dc.language.isoen
dc.publisherWorld Scientific and Engineering Academy and Society
dc.sourceWSEAS Transactions On Information Science and Applications, July 2004, Vol. 1, No. 1, pp. 547-552.
dc.subjectadaptive clustering
dc.subjectknowledge discovery
dc.subjectprediction
dc.subjectneural networks
dc.subjectpruning
dc.subjecttraining
dc.subjectretinol and beta-carotene plasma levels
dc.subjectComputer Science
dc.titlePredicting Trends in Retinol and Beta-Carotene Plasma Levels Using Neural Networks
dc.typeArticle

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