Neural network and regression predictions of 5-year survival after colon carcinoma treatment.
Snow PB., Kerr DJ., Brandt JM., Rodvold DM.
BACKGROUND: The Commission on Cancer data from the National Cancer Data Base (NCDB) for patients with colon carcinoma was used to develop several artificial neural network and regression-based models. These models were designed to predict the likelihood of 5-year survival after primary treatment for colon carcinoma. METHODS: Two modeling methods were used in the study. Artificial neural networks were used to select the more important variables from the NCDB database and model 5-year survival. A standard parametric logistic regression also was used to model survival and the two methods compared on a prospective set of patients not used in model development. RESULTS: The neural network yielded a receiver operating characteristic (ROC) area of 87.6%. At a sensitivity to mortality of 95% the specificity was 41%. The logistic regression yielded a ROC area of 82% and at a sensitivity to mortality of 95% gave a specificity of 27%. CONCLUSIONS: The neural network found a strong pattern in the database predictive of 5-year survival status. The logistic regression produced somewhat less accurate, but good, results.