Neural networks and survival analysis (abstract only)
Helen Wong · ACM SIGBIO Newsletter · 2000
Estimate the survival function and predicting the event occurrence has been the center of interest in medical statistic area. Currently, a set of 1,616 breast cancer patients was given by Manchester Christie hospital. Here we investigate different methods, which able to estimate the survival rate of patients after surgery for 5 years accurately, in a way to improve their quality of life and also detect the important factors, which may affect the prognosis accuracy. Cox regression, known as the proportional hazard model , is the most conventional and widely used method for censored survival data. Censorship is a feature of survival data, the endpoint of individuals are not the event of interest. Neural network model has been considered as an alternative model to handle survival data. It has been used for regression and classification problem in several areas, such as engineering, biomedicine, etc. Breast cancer is one of the deadly diseases for women from the last century. In this study, we demonstrated how the neural networks handle censorship, overcome over-fitting problem and avoid significant bias that introduced when using Bayesian framework in stewed data condition. Patients were grouped into different prognostic groups using prognostic indexes and estimate their survival rate for each groups. The result of neural network model was comparable to the Cox regression.