Question

Artficial neural network in solving biological problems

Answer 1

Artificial neural networks have been shown to be effective as general non-linear models with applications to medical diagnosis, prognosis and survival analysis. This chapter begins with a review of artificial neural networks used as non-linear regression models in the survival analysis of breast cancer patients. These techniques are of much interest because they allow modelling of time-dependent hazards in the presence of complex non-linear and non-additive effects between covariates. First, the role of neural networks is introduced within the context of statistical methods and parametric techniques for prognosis of survival in breast cancer. Second, these methods are applied in a study comprising node-negative breast cancer patients in order to evaluate the evidence for improved models or combination of prognostic indices to be used in a clinical environment. In particular, node-negative breast cancer is an early form of breast cancer in which cancer cells have not yet spread to the regional lymph nodes. There is much interest in determining the relevant prognostic factors that can allocate node-negative patients into prognostic groups correlating with the risk of disease relapse and mortality following surgery. This risk index can then be used to inform the choice of therapy. The Cox regression model and Artificial Neural Networks (ANN), a Partial Logistic Artificial Neural Network with Automatic Relevance Determination (PLANN-ARD) are used in order to identify and interpret the prognostic group allocation. A monthly retrospective cohort study with 5-year follow-up is conducted in pathologically node-negative patients selected from two datasets collected from Manchester Christie Hospital, UK.