CAJ | 학술논문

Objective: Support Vector Machine (SVM) is a machine-learning method, based on the principle of structural risk minimization, which performs well when applied to data outside the training set. In this paper, SVM was applied to predict 5-year survival status of patients with nasopharyngeal carcinoma (NPC) after treatment, we expect to find a new way for prognosis studies in cancer so as to assist right clinical decision for individual patient. Methods: Two modelling methods were used in the study; SVM network and a standard parametric logistic regression were used to model 5-year survival status. And the two methods were compared on a prospective set of patients not used in model construction via receiver operating characteristic (ROC) curve analysis. Results: The SVM1, trained with the 25 original input variables without screening, yielded a ROC area of 0.868, at sensitivity to mortality of 79.2% and the specificity of 94.5%. Similarly, the SVM2, trained with 9 input variables which were obtained by optimal input variable selection from the 25 original variables by logistic regression screening, yielded a ROC area of 0.874, at a sensitivity to mortality of 79.2% and the specificity of 95.6%, while the logistic regression yielded a ROC area of 0.751 at a sensitivity to mortality of 66.7% and gave a specificity of 83.5%. Conclusion: SVM found a strong pattern in the database predictive of 5-year survival status. The logistic regression produces somewhat similar, but better, results. These results show that the SVM models have the potential to predict individual patient's 5-year survival status after treatment, and to assist the clinicians for making a good clinical decision.