Oscar R Marin

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Currently, the best way to reduce the mortality of cancer is to detect and treat it in its early stages. Automatic decision support systems, such as automatic diagnosis systems, are very helpful in this task but their performance is constrained by the integrity of the clinical input data. This could be a problem since clinical databases, in which these(More)
In this paper, we show the results of a study in which we try to test the feasibility of using radial basis functions neural networks (RBFs for short) in clinical decision support systems. We have implemented two instances of RBFs in order to diagnose possible prostate cancer cases from a clinical database. To give an idea about how good the results are, we(More)
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