Improving Gastric Cancer Outcome Prediction Using Single Time-Point Artificial Neural Network Models.
作者： Hamid Nilsaz-Dezfouli , Mohd Rizam Abu-Bakar , Jayanthi Arasan , Mohd Bakri Adam , Mohamad Amin Pourhoseingholi
关键词:
摘要: In cancer studies, the prediction of outcome based on a set prognostic variables has been long-standing topic interest. Current statistical methods for survival analysis offer possibility modelling survivability but require unrealistic assumptions about time distribution or proportionality hazard. Therefore, attention must be paid in developing nonlinear models with less restrictive assumptions. Artificial neural network (ANN) are primarily useful when approaches required to sift through plethora available information. The applications ANN and diagnostic classification medicine have attracted lot patients gastric discussed some studies without completely considering censored data. This study proposes an model predicting survivability, Five separate single time-point were developed predict after 1, 2, 3, 4, 5 years. performance probabilities death is consistently high all points according accuracy area under receiver operating characteristic curve.
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