Artificial neural networks for diagnosis and survival prediction in colon cancer.
ANNs are nonlinear regression computational devices that have been used for over 45 years in classification and survival prediction in several biomedical systems, including colon cancer. Described in this article is the theory behind the three-layer free forward artificial neural networks with backpropagation error, which is widely used in biomedical fields, and a methodological approach to its application for cancer research, as exemplified by colon cancer. Review of the literature shows that applications of these networks have improved the accuracy of colon cancer classification and survival prediction when compared to other statistical or clinicopathological methods. Accuracy, however, must be exercised when designing, using and publishing biomedical results employing machine-learning devices such as ANNs in worldwide literature in order to enhance confidence in the quality and reliability of reported data.
Ahmed
Farid E
FE
Department of Radiation Oncology, Leo W Jenkins Cancer Center, The Brody School of Medicine, East Carolina University, Greenville, NC 27858, USA. ahmedf@mail.ecu.edu
D003110:Colonic Neoplasms / Q000175:diagnosis*; D006801:Humans; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D011379:Prognosis; D012680:Sensitivity and Specificity; D015996:Survival Rate