Comparing the predictive value of neural network models to logistic regression models on the risk of death for small-cell lung cancer patients.
Cancer is one of the leading causes of mortality in the developed world, and prognostic assessment of cancer patients is indispensable in medical care. Medical researchers are accustomed to using regression models to predict patient outcomes. Neural networks have been proposed as an alternative with great potential. Nonetheless, empirical evidence remains lacking to support the application of this technique as the appropriate method to investigate cancer prognosis. Utilizing data on patients from two National Cancer Institute of Canada clinical trials, we compared predictive accuracy of neural network models and logistic regression models on risk of death of limited-stage small-cell lung cancer patients. Our results suggest that neural network and logistic regression models have similar predictive accuracy. The distributions of individual predicted probabilities are very similar. On occasion, however, the prediction pairs are quite different, suggesting that they do not always give the same interpretations of the same variables.
Bartfay
E
E
University of Ontario Institute of Technology, Oshawa, Ontario, Canada. emma.bartfay@uoit.ca
Mackillop
W J
WJ
Pater
J L
JL
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D018288:Carcinoma, Small Cell / Q000401:mortality*; D005260:Female; D006801:Humans; D016015:Logistic Models; D008175:Lung Neoplasms / Q000401:mortality*; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011379:Prognosis; D012372:ROC Curve; D018570:Risk Assessment / Q000379:methods