A Novel Intelligent Hybrid Optimized Analytics and Streaming Engine for Medical Big Data.

Autor: Thilagaraj M; Department of Electronics and Instrumentation Engineering, Karpagam College of Engineering, Coimbatore, India., Dwarakanath B; Department of Information Technology, SRM Institute of Science and Technology, Ramapuram Campus, Bharathi Salai, Ramapuram, Chennai, 600 089 Tamil Nadu, India., Pandimurugan V; School of Computing Science and Engineering, VIT Bhopal University, Kotri Kalan, Ashta, Near, Indore Road, Bhopal, Madhya Pradesh 466114, India., Naveen P; Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore, India., Hema MS; Anurag University, School of Engineering, Department of Information Technology, Venkatapur, Ghatkesar Rd, Hyderabad, Telangana 500088, India., Hariharasitaraman S; School of Computing Science and Engineering, VIT Bhopal University, Kotri Kalan, Ashta, Near, Indore Road, Bhopal, Madhya Pradesh 466114, India., Arunkumar N; Department of Biomedical Engineering, Rathinam Technical Campus, Coimbatore 641021, India., Govindan P; Department of Electrical and Electronics Technology, Ethiopian Technical University, Addis Ababa, Ethiopia.
Jazyk: angličtina
Zdroj: Computational and mathematical methods in medicine [Comput Math Methods Med] 2022 Mar 17; Vol. 2022, pp. 7120983. Date of Electronic Publication: 2022 Mar 17 (Print Publication: 2022).
DOI: 10.1155/2022/7120983
Abstrakt: 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.
Competing Interests: The authors declare no conflicts of interest.
(Copyright © 2022 M. Thilagaraj et al.)
Databáze: MEDLINE
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