Evolved bayesian networks as a versatile alternative to partin tables for prostate cancer management

In this paper, we report on work done evolving Bayesian Networks with Genetic Algorithms. We use a Chain Model GA [19] to induce a Bayesian network model for the real world problem of Prostate Cancer management. Bayesian networks can and have been used in a wide range of complex domains, notably in medicine. In fact, they have shown powerful capabilities in representing and dealing with the uncertainties generally inherent in the clinical practice. In this study, we investigate those capabilities by testing the evolved model's predictive power and exploring its potential use as a more versatile alternative to the widely used Partin tables for prostate cancer pathology staging.

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