Publication

Abstract : Artificial intelligence (AI) has been shown to be a formidable instrument in managing Big Healthcare Data, and it has seen considerable success in bioinformatics. The advancement of big data in biological sciences has given rise to big data analytics (BDA) and artificial intelligence (AI). Because the AI methodologies used in bioinformatics are parallel and iterative, scalable big data management employing distributed and parallel technology is possible. The growth of bioinformatics has resulted in significant storage and administration issues; to share information, such large amounts of data must be handled efficiently. Computational developments in information technology have enabled analytical systems to cope with such data. Therefore, this study emphasizes the impact of big data and BDA in bioinformatics. A practical use of BDAs and AI in cancer classification was given, combining a unique Analysis of Variance (ANOVA) approach with Ant Colony Optimization (ACO) as a hybrid feature selection to pick significant genes while minimizing gene redundancy. Deep Convolutional Neural Networks (DCNN) were employed to classify the datasets. It is because microarray data are produced from gene expression data, and it frequently has a limited number of samples but a huge feature collection size. Using the same datasets, the suggested system outperformed earlier state-of-the-art approaches. The results of the proposed model on all the Leukemia, DLBCL, Colon, and SRDCT datasets revealed an average classification accuracy of 97.7%, 99.9%, 99.9% and 100%, respectively.