title: Neural network and regression predictions of 5-year survival after colon carcinoma treatment.
abstract: 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.
authors: Xaim, Inc., Colorado Springs, Colorado, USA. psnow@xaim.com
authors: P B
authors: PB
authors: Snow
authors: D J
authors: DJ
authors: Kerr
authors: J M
authors: JM
authors: Brandt
authors: D M
authors: DM
authors: Rodvold
mesh_terms: D000368:Aged; D002277:Carcinoma / Q000401:mortality* / Q000473:pathology / Q000628:therapy*; D003110:Colonic Neoplasms / Q000401:mortality* / Q000473:pathology / Q000628:therapy*; D016208:Databases, Factual; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011379:Prognosis; D011446:Prospective Studies; D012044:Regression Analysis; D012680:Sensitivity and Specificity; D016019:Survival Analysis