Application of artificial neural network-based survival analysis on two breast cancer datasets.
This paper applies artificial neural networks (ANNs) to the survival analysis problem. Because ANNs can easily consider variable interactions and create a non-linear prediction model, they offer more flexible prediction of survival time than traditional methods. This study compares ANN results on two different breast cancer datasets, both of which use nuclear morphometric features. The results show that ANNs can successfully predict recurrence probability and separate patients with good (more than five years) and bad (less than five years) prognoses. Results are not as clear when the separation is done within subgroups such as lymph node positive or negative.
Chi
Chih-Lin
CL
Health Informatics Program, University of Iowa, USA.
Street
W Nick
WN
Wolberg
William H
WH
D001943:Breast Neoplasms / Q000401:mortality* / Q000601:surgery; D019992:Databases as Topic; D003661:Decision Support Techniques; D018572:Disease-Free Survival; D006801:Humans; D053208:Kaplan-Meier Estimate; D008954:Models, Biological; D009364:Neoplasm Recurrence, Local; D016571:Neural Networks, Computer; D011379:Prognosis; D018709:Statistics, Nonparametric; D016019:Survival Analysis