Microarray based disease aanalysis using spatial EM and SVMM classification algorithm

Diseases classification using gene expression data is known to include the keys for addressing the fundamental harms relating to diagnosis and discovery. The recent introduction of DNA microarray technique has complete simultaneous monitoring of thousands of gene expressions possible. With this large quantity of gene expression data, researchers have started to discover the possibilities of disease classification using gene expression data. Quite a number of methods have been planned in recent years with hopeful results. But there are still a lot of issues which need to be address and understood. In order to gain insight into the disease classification difficulty, it is necessary to take a closer look at the problem, the proposed solutions and the associated issues all together. In this paper, we present a comprehensive clustering method and classification method such as Spatial Expectation Maximization, Support Vector classification and estimate them based on their calculation time, classification accuracy and ability to reveal biologically meaningful gene information. Based on our multiclass classification method to diagnosis the diseases and also find severity levels of diseases. Our experimental results show that classifier performance through graphs with improved accuracy.