Volume 6, Number 3 (11 2010)                   irje 2010, 6(3): 22-27 | Back to browse issues page

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Biglarian A, Hajizadeh E, Kazemnejad A. Comparison of Artificial Neural Network and Parametric Regression Models in Survival Prediction of Patients with Gastric Cancer. irje. 2010; 6 (3) :22-27
URL: http://irje.tums.ac.ir/article-1-74-en.html

Abstract:   (9621 Views)
Background & Objective: Using parametric models is common approach in survival analysis. In the recent years, artificial neural network (ANN) models have increasingly used in survival prediction. The aim of this study was to predict of survival rate of patients with gastric cancer by using a parametric regression and ANN models and compare these methods.
Methods: We used the data of 436 gastric cancer patients from a cancer registry in Tehran between 2002-2007. All patients had a confirmed diagnosis. Data were randomly divided into two groups: training and testing (or validation) set. For analysis of data we used a parametric model (exponential, Weibull, normal, lognormal, logistic and log-logistic models) and a three layer ANN model. In order to compare of the prediction of two models, we used the area under receiver operating characteristic (AUROC) curve, classification table and concordance index.
Results: The prediction accuracy of the ANN and the parametric (Weibull) models were 79.45% and 73.97% respectively. The AUROC for the ANN and the Weibull models were 0.815 and 0.748 respectively.
Conclusions: The ANN had a better predictions than the Weibull model. Thus it is suggested to use of the ANN model survival prediction in field of cancer.
Full-Text [PDF 172 kb]   (1867 Downloads)    
Type of Study: Research | Subject: General
Received: 2009/12/7 | Accepted: 2010/07/3 | Published: 2013/09/1

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