文章基本信息

标题：Artificial Neural Network Model for Predicting Lung Cancer Survival
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作者：Hansapani Rodrigo ; Chris P. Tsokos
期刊名称：Journal of Data Analysis and Information Processing
印刷版ISSN：2327-7211
电子版ISSN：2327-7203
出版年度：2017
卷号：05
期号：01
页码：33-47
DOI：10.4236/jdaip.2017.51003
语种：English
出版社：Scientific Research Publishing
摘要：The object of our present study is to develop a piecewise constant hazard model by using an Artificial Neural Network (ANN) to capture the complex shapes of the hazard functions, which cannot be achieved with conventional survival analysis models like Cox proportional hazard. We propose a more convenient approach to the PEANN created by Fornili et al. to handle a large amount of data. In particular, it provides much better prediction accuracies over both the Poisson regression and generalized estimating equations. This has been demonstrated with lung cancer patient data taken from the Surveillance, Epidemiology and End Results (SEER) program. The quality of the proposed model is evaluated by using several error measurement criteria.
关键词：Survival Analysis;Hazard Prediction;Artificial Neural Network;Piecewise Exponential Survival Model;Censored Data;Lung Cancer