On the misuses of artificial neural networks for prognostic and diagnostic classification in oncology

Application of machine learning algorithms for clinical predictive modeling: a data-mining approach in SCT

“…Numerous publications discuss the pros and cons of DM in depth. 7,9,59 In conclusion, given the power of the data-mining approach to process a multiplicity of variables, describe complex non-linear interactions and create accurate prediction models, it seems natural to apply it for the complex analysis of HSCT databases. So far, lack of interpretability and experience with the different models have deterred clinical researchers and physicians.…”

The Application of Artificial Intelligence to Microarray Data: Identification of a Novel Gene Signature to Identify Bladder Cancer Progression

“…Various AI techniques exist [8] and successful microarray analysis has been reported using artificial neural networks (ANN) [9] [10] and support vector machines (SVMs) [11,12] in non-urothelial malignancies. However, the hidden working layer of an ANN prevents model understanding and hinders its acceptance by the scientific community [13], whilst SVMs still use proximity to infer class-gene associations and function poorly with respect to interpretability [14].…”

Survival Analysis Part IV: Further concepts and methods in survival analysis

“…Biganzoli et al (1998) and others have modelled the hazard functions directly, in a promising attempt to extend this method. Reviews comparing the examples where both ANN and regression methods had been used to derive prognostic models have found that overall ANNs are little better than classical statistical modelling approaches (Sargent, 2001), and misuses of ANNs in oncology are common (Schwarzer et al, 2000). We therefore advise caution in their use, and the involvement of an experienced statistician.…”