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Neural network and regression predictions of 5‐year survival after colon carcinoma treatment
Author(s) -
Snow Peter B.,
Kerr David J.,
Brandt Jeffrey M.,
Rodvold David M.
Publication year - 2001
Publication title -
cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.052
H-Index - 304
eISSN - 1097-0142
pISSN - 0008-543X
DOI - 10.1002/1097-0142(20010415)91:8+<1673::aid-cncr1182>3.0.co;2-t
Subject(s) - logistic regression , artificial neural network , medicine , receiver operating characteristic , regression , cancer , regression analysis , artificial intelligence , statistics , computer science , mathematics
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. Cancer 2001;91:1673–8. © 2001 American Cancer Society.