The Case Study of Cancer Diagnosis System Based on Kernel Method and Genetic Approach

摘要

One of the most important problems in bioinformatics is how to extract the useful information from a huge amount of data, and make decision in diagnosis, prognosis, and medical treatment applications. To deal with this problem, many approaches have been proposed. The main goal of our study is to figure out an efficient method to achieve a cancer diagnosis system with high accuracies, and good adaptability to various types of data set. For accomplishing this goal, we proposed a new kernel function which is defined as the weighted sum of a set of different types of basic kernel functions and also its learning method based on GA. The experimental results on clinical datasets are proved that our proposed approach with the novel kernel function and its learning method has a higher prediction rate, comparable and sometimes better performance than the previous ones.