Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> F. RQ5. WHAT ARE THE MEDICAL BIG DATA ANALYTICS IN CARDIOLOGY? <s> Advances in high-throughput and mass-storage technologies have led to an information explosion in both biology and medicine, presenting novel challenges for analysis and modeling. With regards to multivariate analysis techniques such as clustering, classification, and regression, large datasets present unique and often misunderstood challenges. The authors' goal is to provide a discussion of the salient problems encountered in the analysis of large datasets as they relate to modeling and inference to inform a principled and generalizable analysis and highlight the interdisciplinary nature of these challenges. The authors present a detailed study of germane issues including high dimensionality, multiple testing, scientific significance, dependence, information measurement, and information management with a focus on appropriate methodologies available to address these concerns. A firm understanding of the challenges and statistical technology involved ultimately contributes to better science. The authors further suggest that the community consider facilitating discussion through interdisciplinary panels, invited papers and curriculum enhancement to establish guidelines for analysis and reporting. <s> BIB001 </s> Big Data Features, Applications, and Analytics in Cardiology—A Systematic Literature Review <s> F. RQ5. WHAT ARE THE MEDICAL BIG DATA ANALYTICS IN CARDIOLOGY? <s> The potential for big data analytics to improve cardiovascular quality of care and patient outcomes is tremendous. However, the application of big data in health care is at a nascent stage, and the evidence to date demonstrating that big data analytics will improve care and outcomes is scant. This Review provides an overview of the data sources and methods that comprise big data analytics, and describes eight areas of application of big data analytics to improve cardiovascular care, including predictive modelling for risk and resource use, population management, drug and medical device safety surveillance, disease and treatment heterogeneity, precision medicine and clinical decision support, quality of care and performance measurement, and public health and research applications. We also delineate the important challenges for big data applications in cardiovascular care, including the need for evidence of effectiveness and safety, the methodological issues such as data quality and validation, and the critical importance of clinical integration and proof of clinical utility. If big data analytics are shown to improve quality of care and patient outcomes, and can be successfully implemented in cardiovascular practice, big data will fulfil its potential as an important component of a learning health-care system. <s> BIB002
Analytical goal of big data in healthcare is the modelling, prediction, and inference, clustering, classification, and regression are general approaches exploited BIB001 . The classification of medical big data is a kind of supervised learning and is brought as analytical modelling. The analytics of big data can help and support in the early on accurate prediction of heart disease, and discovery of variation from healthy state. Rumsfeld et al. BIB002 categorized eight areas of big data analytics for the improvement of healthcare. These areas are; 1) analytical modelling for risk and usage of resources; 2) disease and treatment heterogeneity; 3) surveillance of drug and device safety in healthcare; 4) management of population; 5) measurement of performance and quality care; 6) clinical decision support and precision medicine; 7) public health; and 8) application of research. Analytics of big data and applications of ML is used in cardiology for many reasons, such as for clinical decision support, detection of risk factor for cardiovascular disease, and for precision medicine using genomic information  . Table 10 shows the representation of the research work identified by this study big data analytics in healthcare especially in cardiology.