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

Date

2004-07-01

Authors

Kaikhah, Khosrow

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World Scientific and Engineering Academy and Society

Abstract

A 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.

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Keywords

adaptive clustering, knowledge discovery, prediction, neural networks, pruning, training, retinol and beta-carotene plasma levels, Computer Science

Citation

Kaikhah, 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.

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