The Study On Designing And Optimizing The Architecture Of Back Propagation Artificial Neural Network And The Application In Medical Statistics

Artificial neural network (ANN) is a rising borderline science. Compared to the mathematical statistics, it doesn't need exact mathematical model and dose not have any consumption (such as distribution and independence) demanding the variables to meet. It can make up the deficiency of mathematical statistical methods and solve some problems that traditional statistical methods failed to resolve.In medical study fields, the application of ANN is more and more popular. Particularly attention had been given to how to build a suitable ANN model to resolve the actual problems. This study focuses on the back propagation network (BP network) which is the most popular model used in medical filed. The fundamental of BP network was introduced and the description from the statistical point of view was given. Several simulative BP network architectures had been set up to discuss the designing, learning, optimizing and evaluation of the BP networks. The details of some simple optimizing methods, such as how to design the number of hidden units, prune the network to improve its generalization, and stop learning properly were studied. The performance and relation to statistics of these methods had also been discussed.There were three parts of the main study.The BP model without hidden layer (also can be taken as single layer model) was constructed and its principles of architecture designing and learning process and the methods of evaluation were summarized, which also can be used in multi-layer BP model and can be used as a conduct in application. Residual analysis and AUC ofROC were introduced as methods of evaluation. For the particular meaning of single layer BP model using in risk factor screening in medicine, a simulating study to derive the approximation bounds of its learning sample was conducted. Only the multi-layer BP model' bound had been studied before by other scholars. The outcome of simulation indicated that a stable model with proper ability of generalization had been derived when the relationship between sample size and links number is 10:1.For multi-layer BP model, we focused on the study of hidden layer. Description of the function of hidden layer was given. Information theory was inducted into the study of the number of hidden units. A novel method using entropy to estimate the number of hidden units was proposed. The simulation outcome showed that the effect of BP model with the number of hidden units generated by entropy performed better than other model.To simplifying the structure, the effect of pruning algorithms application in BP network model and its relationship with medical statistics were studied. In our study, one of the pruning algorithms - optimal brain damage method was applied in multi-variable data analysis. It is the first time that the application in variable selection with this method was discussed. We found that pruning in single layer BP network model can act as variable selection, and the weight of BP network model after pruning had the same meaning as regression coefficient. Thus a new method for the hazard factor screening in medical study has been proposed.Those principles of BP network designing and optimization were also applied on actual medical prognosis data and survival data. The result of that was also compared with the performance of logistic regression to test and verify.The innovation of this study lied in the following: Inducting the entropy in information theory into the BP network architecture constructing and bring a novel method of hidden units number designing using entropy. The pruning algorithm being used in medical data as a method of variable selection for the first time. The approximation bounds of single layer BP model being studied bysimulated data.For the limit of time and data collection, the study on actual survival data was only conducted by one architecture model. The further study should be conducted on the analysis of other models and the censored data treatment should be considered. The entropy method of...