AN ARTIFICIAL NEURAL NETWORK MODEL FOR OUTCOME PREDICTION IN GASTRIC CANCER PATIENTS
Hamid Nilsaz-Dezfouli, Mohd Rizam Abu-Bakar, Mohamad Amin Pourhoseingholi · Journal of Fundamental and Applied Sciences · 2016
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.