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