Evolutionary assembled neural networks for making medical decisions with minimal regret: Application for predicting advanced bladder cancer outcome
A novel two-step procedure for obtaining reliable ANN predictive models is presented. Optimal configuration of ANN was performed automatically using Genetic Algorithms. Clinical utility was estimated by integrating the Regret Theory Decision Curve Analysis into the procedure. For predicting of advanced bladder cancer outcome soft-max activation functions and good calibration are the most important. Compared to the alternatives better prognostic performances were achieved while user-dependency was significantly reduced.
bladder cancer genetic algorithms regret theory expert systems artificial neural network