A Novel Intelligent Hybrid Optimized Analytics and Streaming Engine for Medical Big Data. (17th March 2022)

• Record Type: Journal Article
• Authors: Thilagaraj, M.; Dwarakanath, B.; Pandimurugan, V.; Naveen, P.; Hema, M. S.; Hariharasitaraman, S.; Arunkumar, N.; Govindan, Petchinathan
• Other Names: Koundal Deepika Academic Editor.
• Is Part Of: Computational and mathematical methods in medicine. Volume 2022(2022)
• Publication Date: 2022-03-17
• Subjects: Medicine -- Computer simulation -- Periodicals; Medicine -- Mathematical models -- Periodicals; 610.11
• DOI: 10.1155/2022/7120983
• Languages: English
• ISSNs: 1748-670X

Abstract

Abstract : Medical data processing is exponentially increasing day by day due to the frequent demand for many applications. Healthcare data is one such field, which is dynamically growing day by day. In today's scenario, an enormous amount of sensing devices and data collection units have been employed to generate and collect medical data all over the world. These healthcare devices will result in big real-time data streams. Hence, healthcare-based big data analytics and monitoring have gained hawk-eye importance but needs improvisation. Recently, machine and deep learning algorithms have gained importance to analyze huge amounts of medical data, extract the information, and even predict the future insights of diseases and also cope with the huge volume of data. But applying the learning models to handle big/medical data streams remains to be a challenge among the researchers. This paper proposes the novel deep learning electronic record search engine algorithm (ERSEA) along with firefly optimized long short-term memory (LSTM) model for better data analytics and monitoring. The experimentations have been carried out using Apache Spark using the different medical respiratory data. Finally, the proposed framework results are contrasted with existing models. It shows the accuracy, sensitivity, and specificity like 94%, 93.5%, and 94% for less than 5 GB dataset, and also, more than 5 GB it provides 94%, 92%, and 93% to prove the extraordinary performance of the proposed framework.