Dynamically weighted evolutionary ordinal neural network for solving an imbalanced liver transplantation problem
作者：
Highlights：
• The problem of constructing an organ allocation decision-support system combining donor, recipiend and surgery characteristics using artificial neural networks is assessed.
• A dynamically weighted evolutionary algorithm alleviates the imbalanced nature of the dataset.
• Ordinal over-sampling techniques help balancing the training set and improve the results obtained by the classifiers.
• An ordinal artificial neural network has been proven to perform well for the 4-category classification problem assessed.
• An extended supranational experimental design for liver transplantation allocation could be feasible considering more countries.
摘要
Highlights•The problem of constructing an organ allocation decision-support system combining donor, recipiend and surgery characteristics using artificial neural networks is assessed.•A dynamically weighted evolutionary algorithm alleviates the imbalanced nature of the dataset.•Ordinal over-sampling techniques help balancing the training set and improve the results obtained by the classifiers.•An ordinal artificial neural network has been proven to perform well for the 4-category classification problem assessed.•An extended supranational experimental design for liver transplantation allocation could be feasible considering more countries.
论文关键词：Artificial neural networks,Ordinal classification,Imbalanced classification,Survival analysis,Liver transplantation
论文评审过程：Received 21 July 2016, Revised 17 January 2017, Accepted 5 February 2017, Available online 20 February 2017, Version of Record 9 March 2017.
论文官网地址：https://doi.org/10.1016/j.artmed.2017.02.004
