Hamid Nilsaz-Dezfouli - Institute for Mathematical Research Universiti Putra Malaysia, 43400 UPM Serdang, Malaysia
Mohd Rizam Abu-Bakar - Institute for Mathematical Research Universiti Putra Malaysia, 43400 UPM Serdang, Malaysia
Navid Nilsaz - Payam Noor Fars University
Mohammad Amin Pourhoseingholi - Department of Health System Research, Gastroenterology and Liver Diseases Research Center, Shahid Beheshti University of Medical Sciences, Tehran, IR Iran

Multiclass pattern recognition is a problem of building a system that accurately maps an input feature space to an output space of more than two pattern classes. K-class pattern classification can be implemented in a single neural network with K output nodes. Such a model can be extended to make predictions about patients‟ probability of survival over time. This paper proposes a multiple time-point ANN model for predicting the probability of survival at different time intervals for patients with gastric cancer. More specifically, survival is modeled using a multiple-output ANN, with a structure modulated to produce different values as the probability of survival for each time interval. The model‟s performance in outcome prediction is investigated with a real gastric cancer data set

Gastric Cancer, Survival Analysis, Artificial Neural Networks