Cardiovascular disease is the predominant cause of death throughout the world. In the present era, many diseases are caused by gene transformation. Asian Indians have a higher chance of having cardiovascular disease as compared to any other global population. Identifying relevant and candidate genes for classification of samples is a tedious task when dealing with gene expression data analysis. The objective of this paper is to find the relevant genes responsible for causing coronary artery disease. In this paper we developed a novel feature selection algorithm based on fold change and p-value. Instead of selecting genes randomly our proposed method selects the top ranking candidate genes responsible for coronary artery disease. The selected differentially expressed genes from the feature selection phase are evaluated using the proposed ensemble classifier. The classifier used in this work are support vector machine, neural network and na¨ıve bayes. The proposed framework is validated by experiments on three publicly available microarray datasets. The results clearly show that the proposed ensemble classifier performs better when compared to other classifiers. The selected candidate genes are used for carrying out diagnostic tests and for classifying the patients, which reduces the cost and also improves the accuracy.
Digital Object Identifier (DOI)
Uma Maheswari, K. and Valarmathi, A.
"A Novel Feature Selection Algorithm for Coronary Artery Disease Prediction,"
Applied Mathematics & Information Sciences: Vol. 12
, Article 8.
Available at: https://dc.naturalspublishing.com/amis/vol12/iss4/8