Survival prediction and treatment recommendation with Bayesian techniques in lung cancer.

In this paper, we investigate a number of Bayesian techniques for predicting 1-year- survival and making treatment selection recommendations for lung cancer. We have carried out two sets of experiments on the English Lung Cancer Dataset. For 1-year-survival prediction, the Naïve Bayes (NB) algorithm achieved an area under the curve value of 81%, outperforming the Bayesian Networks learned by the M(3) and K2 structure learning algorithms. For treatment recommendation, the Bayesian Network, whose structure was learned by the MC(3) algorithm, has marginally outperformed NB, based on producing concordant results with the recorded treatments in the dataset. We observed that in cases where the classifier recommendations were discordant with the recorded treatments, the 1-year-survival rate decreased by 15%. We also observed that discordance between the classifier and the dataset was more dominant in cases where the recorded treatment was non-curative or was not frequently encountered in the dataset.