Chapter 11 - Cognitive big data analysis for E-health and telemedicine using metaheuristic algorithms

Abstract

The healthcare industries yield the most critical and massive amount of data from various sources such as biomedical research, hospital records, clinical records of patients, clinical examination results, and different health IoT devices. These data require proper management and analysis to produce meaningful information. Managing and analyzing this vast dataset is very time consuming and expensive with conventional methods. Therefore, providing relevant solutions for improving healthcare services; industries are hunting for lower costs, better outcomes, and value-based solutions to generate and analyze this big data systematically. Thus, an emerging technology called cognitive computing can handle this vast amount of data and produce a smart healthcare solution. Cognitive computing facilitates humans to collaborate with machines to gain actionable insights. It is the most advanced technology in the present scenario, mainly due to the amalgamation of Big Data Analytics with Artificial Intelligence and their allied technologies such as Machine Learning and Deep Learning. These advents have strongly supported the growth of the healthcare field. Cognitive computing works better with massive data. The more data we supply, the more accurate its outputs are. However, implementing cognitive big data for healthcare is complex, and the performance is not efficient. Metaheuristic algorithms are the key to enabling any technology to self-improve their performance. This chapter will comprehensively discuss the past, present, and future directions in big data and cognitive computing for healthcare industries. It will also discuss the optimization of cognitive big data healthcare techniques using metaheuristic approaches.