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Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics


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and foremost, we express our heartfelt appreciation to all the authors. We thank them all for considering and trusting this edited book as the platform for publishing their valuable work. We also thank all the authors for their kind co-operation extended during the various stages of processing of the manuscript. This edited book will serve as a motivating factor for those researchers who have spent years working as crime analysts, data analysts, statisticians, and budding researchers.

      Dr. Sunil Kumar Dhal Professor IT, Sri Sri University, Odisha, India

      Dr. Subhendu Kumar Pani Principal, Krupajal Computer Academy, BPUT, India

      Dr. Srinivas Prasad GITAM Institute of Technology, Visakhapatnam Campus, India

      Dr. Sudhir Kumar Mohapatra Addis Ababa Science and Technology University, Addis Ababa, Ethiopia March 2022

      An Introduction to Big Data Analytics Techniques in Healthcare

       Anil Audumbar Pise*

       University of the Witwatersrand, Johannesburg, South Africa

       Abstract

      There is a notable rise in the amount of data being generated in the healthcare industries. Trying to improve the health outcomes and cut the costs derived from better utilization of healthcare data has been of great interest to healthcare providers (and the abundance of the data has brought that about big change), whereas the nature of healthcare data presents specific problems when it comes to processing and looking at big data, particularly, as well as analyzing the abundance of it. Some new ideas about how to deal with these problems are discussed in this chapter. According to this chapter, there are two ways in which advances in processing healthcare data have been made in the last 10 years that may make generating better predictions from the medical data feasible. Firstly by using advancing technological methods of analysis and secondly developing novel models that can handle large quantities of data.

      Keywords: Healthcare analytics, predictive analytics, healthcare informatics, big data

      Although big data analytics is relatively new in its role in-flux in healthcare, it is nevertheless having a significant impact in practices and research. The system has given healthcare researchers the ability to gather, store, and manage disparate, structured, and unstructured data generated by current healthcare systems, as well as data sets for analysis. Larger databases and powerful computer software have recently been used in medical research to help with delivery and disease exploration. Some of the most basic big data principles cannot be escaped, even though advances have been made; as long as there are these limitations, they may persist in preventing further development in this sector. A concern that we wanted to tackle in this paper is the obstacles we encounter in three exciting new and emergent medical research areas: Genomic Data Analysis, Signal Detection, and Medical Image Processing. In the most recent studies, the focus has been on employing high volume data of medical information, which integrates multimodal information from diverse sources. In order to evaluate the capabilities and opportunities for healthcare delivery, research focuses on areas with the ability to make a positive difference as well as well as potential.

      1 1. Variety: Without a doubt, the variety of data represents big data. For instance, among the various data formats are database, excel, and CSV, all of which can be stored in a plain text file. Additionally, structured, unstructured,