MAGED: Metaheuristic Approach on Gene Expression Data: Predicting the Coronary Artery Disease and the Scope of Unstable Angina and Myocardial Infarction
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 coronary 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. In order to find the confidence of the relation between gene and gene expression a bipartite graph is built between them. The experimental study evincing that the prediction performance of the proposed model is substantial that compared to other benchmarking models.
