MAGED: Metaheuristic Approach on Gene Expression Data: Predicting the Coronary Artery Disease and the Scope of Unstable Angina and Myocardial Infarction

MAGED: Metaheuristic Approach on Gene Expression Data: Predicting the Coronary Artery Disease and the Scope of Unstable Angina and Myocardial Infarction

E.Neelima

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M.S.Prasad Babu

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GITAM University

MAGED: Metaheuristic Approach on Gene Expression Data: Predicting the Coronary Artery Disease and the Scope of Unstable Angina and Myocardial Infarction

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Abstract

The Genetic risk prediction strategies found in practice for coronary artery disease are not significant to estimate the scope of adverse cardiovascular events such as unstable angina and myocardial infarction. Hence in regard to this objective, this manuscript contributed a metaheuristic approach to predict coro-nary artery disease and the scope of unstable angina and myocardial infarction. The proposed metaheuristic is built from the gene expression data of blood samples collected from patients with coronary artery disease diagnosed, unstable angina and Myocardial Infarction. The data also includes gene expression data collected from the blood samples taken from the people clinically proven as salubrious (healthy). The relation between genes and gene expressions are considered as the state of input to devise the metaheuristic.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

E.Neelima. 2016. \u201cMAGED: Metaheuristic Approach on Gene Expression Data: Predicting the Coronary Artery Disease and the Scope of Unstable Angina and Myocardial Infarction\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 16 (GJCST Volume 16 Issue C4): .

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GJCST Volume 16 Issue C4
Pg. 21- 27

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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Classification

GJCST-C Classification: J.3, H.2.1

Submission ReceivedDecember 14, 2015
Peer Review Double Blind
Handling Editor
Accepted January 1, 2016
Published January 15, 2016

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v1.2

Issue date

November 6, 2016

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Article Matrices

Total Score: 102

Country: India

Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering

Authors: E.Neelima, M.S.Prasad Babu (PhD/Dr. count: 0)

View Count (all-time): 282

Total Views (Real + Logic): 7223

Total Downloads (simulated): 1702

Publish Date: 2016 11, Sun

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Total Views: 7223

Total Downloads: 1702

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