Cardiomyopathy is one of important cause of chronic heart failure which makes heart muscle harder to pump blood to other part of the body which leads to high mortality rate. Hence it is becomes mandatory to diagnosis and predict the disease in order to prevent the person against heart failure. However manual analysis of the disease is highly complex and leads to poor prognosis. In order to alleviate those challenges and predict the disease in early stage, many risk assessment methods has been modeled using machine learning and deep learning paradigms using genome wide association studies. Especially Cardiomyopathy risk assessment through gene expression from microarray data provides excellent results. In this article, various architectures to identify the Cardiomyopathy on gene expression profiles of the GEO databases has been analyzed. Initially gene expression profile is processed using normalization technique to regularize the down regulated and unregulated genes in specified range. Next feature extraction technique to obtain the differentially expressed gene. FurtherPotential biomarker is employed to select the DCM related genes such as MYH6, PTH1R, ADAM15, S100A4CKM, NKX2–5 and ATP2A2 which contains the mutated chromosomes. Finally classifier model is employed to the discriminate the core set of genes with core set of target genes extracted from the diseased patient of the mutated chromosomes related to Cardiomyopathy which is considered as ground truth data. Experimental analysis of various classifier employed to the classify the core set of genes into type of classes of Cardiomyopathy is carried out on interfering the results of the classifier on the cross fold validation. Performance evaluation of the architectures on the mentioned dataset is performed using performance measure.

Cardiomyopathy, Classification, Microarray data, Target Genes, Gene Profiling, Normalization, mRNA