Sangeetha, T and Manikandan, K (2023) AN EXTENSIVE STUDY ON CARDIOMYOPATHY CLASSIFICATION TECHNIQUE USING MICROARRAY DATA. AN EXTENSIVE STUDY ON CARDIOMYOPATHY CLASSIFICATION TECHNIQUE USING MICROARRAY DATA, 38 (1). pp. 3835-3840. ISSN 1004-9037

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: Cardiomyopathy, Classification, Microarray data, Target Genes, Gene Profiling, Normalization, mRNA
Divisions: PSG College of Arts and Science > Department of Computer Science
Depositing User: Mr Team Mosys
Date Deposited: 05 Oct 2024 07:46
Last Modified: 05 Oct 2024 07:46
URI: https://ir.psgcas.ac.in/id/eprint/2313

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