Complexity-Reduced Tumor Classification System using Microarray Gene Expression Dataset

e classification of cancer based on gene expression data is the advancements in DNA Microarray technology and genome sequencing. The important feature is to predict the genes for various diagnosis purposes using such micro-array gene expression dataset and also the gene expressions that are relevant to a particular type of genes. Lot of research works have been carried out to produce a better solution to improve the prediction accuracy of cancer gene prediction. But the analysis results are not up to the convincing level artificial intelligence is exploited to improve the prediction accuracy meanwhile state-of-the art insists necessary enhancements which are essential in the classification module instead in the features module. The enhanced classifier called Principal component analysis used in latter researches is used in this work for the performances comparison of the classifier as a conventional prediction methodology. This work intends to apply the developed classifier and dominant gene prediction methodology to predict extensive set of cancer expression datasets. The experimental study will be carried out by considering the techniques on CNS tumor, colon tumor and ALL_AML Leukemia. The prediction performance of the proposed methodology will be compared against the conventional prediction methodologies and the results will be validated extensively. The method will be implemented in the working platform of MATLAB and the performance will be analysed.