Survivability modelling using Bayesian network for patients with first and secondary primary cancers.BACKGROUND AND OBJECTIVE
 In this study, a Bayesian network (BN) model was proposed to describe the occurrence of two primary cancers and predict the five-year survivability of patients using probabilistic evidence. 
Bayesian network
The proposed model significantly outperformed the back-propagation neural network, logistic regression, support vector machine, and naïve Bayes in terms of sensitivity, which is a critical performance index for the non-survival group.



back-propagation neural network
The proposed model significantly outperformed the back-propagation neural network, logistic regression, support vector machine, and naïve Bayes in terms of sensitivity, which is a critical performance index for the non-survival group.



logistic regression
The proposed model significantly outperformed the back-propagation neural network, logistic regression, support vector machine, and naïve Bayes in terms of sensitivity, which is a critical performance index for the non-survival group.



support vector machine
The proposed model significantly outperformed the back-propagation neural network, logistic regression, support vector machine, and naïve Bayes in terms of sensitivity, which is a critical performance index for the non-survival group.



naïve Bayes