期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2017
卷号：95
期号：16
页码：3811
出版社：Journal of Theoretical and Applied
摘要：Cardio Vascular Disease in terms of coronary artery disease and myocardial infractions are one of the majorly impacting factors towards the mortality rates. The kind of revolutionary developments that has taken place in the genomic diagnosis and the solutions that are developed for diagnosis of heart diseases based on analysis of molecular data of blood cells has improved the accuracy of diagnosis phenomenally. In recent past, analysing gene expression data and using for contemporary misnaming models. Particularly using machine learning strategies to predict and classify the given unlabelled gene expression record. In regard to this a substantial requirement is feature optimization, which is since the overall genes observed in human body are closely 25000 and among them 636 genes are cardio vascular related. Hence, it complexes the process of training the machine learning models using these entire cardio vascular gene features. Hence, this manuscript proposed the usage of ANOVA standard called t-test to select optimal features. The experimental study indicating that the number of optimal features those selected by proposed model is substantially low that compared to the other contemporary models. Divergent classifiers those trained by the features selected through proposal evinced significance in classification accuracy. We compare the results obtained from divergent classifiers those trained by the features selected using proposal and other contemporary model for performance analysis.
