Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review

Abstract

In today's digital world the information surges with the widespread use of the internet and global communication systems. Healthcare systems are also facing digital transformations with the enhancement in the utilization of healthcare information systems, electronic records in medical, wearable, smart devices, handheld devices, and so on. A bulk of data is produced from these digital transformations. The recent increase in medical big data and the development of computational techniques in the field of cardiology enables researchers and practitioners to extract and visualize medical big data in a new spectrum. The role of medical big data in cardiology becomes a challenging task. Early decision making in cardiac healthcare system has massive potential for dropping the cost of care, refining quality of care, and reducing waste and error. Therefore, to facilitate this process a detailed report of the existing literature will be feasible to help the doctors and practitioners in decision making for the purpose of identifying and treating cardiac diseases. This detailed study will summarize results from the existing literature on big data in the field cardiac disease. This research uses the systematic literature protocol as presented by Kitchenham et al. The data was collected from the published materials from 2008 to 2018 as conference or journal publications, books, magazines and other online sources. 190 papers were included relying on the defined inclusion, exclusion, and checking the quality criteria. The current study helped to identify medical big data features, the application of medical big data, and the analytics of the big data in cardiology. The results of the proposed research shows that several studies exist that are associated to medical big data specifically to cardiology. This research summarizes and organizes the existing literature based on the defined keywords and research questions. The analysis will help doctors to make more authentic decisions, whi...