Evolutionary assembled neural networks for making medical decisions with minimal regret: Application for predicting advanced bladder cancer outcome
作者：
Highlights：
• 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.
摘要
•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.
论文关键词：Bladder cancer,Expert systems,Artificial Neural Network,Genetic Algorithms,Regret Theory
论文评审过程：Available online 11 July 2014.
论文官网地址：https://doi.org/10.1016/j.eswa.2014.07.006
