Big Data Visualization in Cardiology—A Systematic Review and Future Directions

Abstract

The digital transformations and use of healthcare information system, electronic medical records, wearable technology, and smart devices are increasing with the passage of time. A variety of sources of big data in healthcare are available, such as biometric data, registration data, electronic health record, medical imaging, patient reported data, biomarker data, clinical data, and administrative data. Visualization of data is a key tool for producing images, diagrams, or animations to convey messages from the viewed insight. The role of cardiology in healthcare is obvious for living and life. The function of heart is the control of blood supply to the entire parts of the body. Recent speedy growth in healthcare and the development of computation in the field of cardiology enable researchers and practitioners to mine and visualize new insights from patient data. The role of visualization is to capture the important information from the data and to visualize it for the easiness of doctors and practitioners. To help the doctors and practitioners, the proposed study presents a detailed report of the existing literature on visualization of data in the field of cardiology. This report will support the doctors and practitioners in decision-making process and to make it easier. This detailed study will eventually summarize the results of the existing literature published related to visualization of data in the cardiology. This research uses the systematic literature protocol and the data was collected from the studies published during the year 2009 to 2018 (10 years). The proposed study selected 53 primary studies from different repositories according to the defined exclusion, inclusion, and quality criteria. The proposed study focused mainly on the research work been done on visualization of big data in the field of cardiology, presented a summary of the techniques used for visualization of data in cardiology, and highlight the benefits of visualizations in cardiology. The current research summarizes and organizes the available literature in the form of published materials related to big data visualization in cardiology. The proposed research will help the researchers to view the available research studies on the subject of medical big data in cardiology and then can ultimately be used as evidence in future research. The results of the proposed research show that there is an increase in articles published yearly wise and several studies exist related to medical big data in cardiology. The derivations from the studies are presented in the paper.

Big Data, Extracting Insights, Comprehension, and Analytics in Cardiology: An Overview

“…The study was presented as an evidence for the researchers and practitioners to devise novel solutions in the area of interest. Nazir et al [ 13 ] presented a comprehensive review of the 10 years from 2008 to 2018 associated with the visualization of big data in the area of cardiology. The study identified 53 prospective papers related to visualization of big data in cardiology.…”

A Systematic Review of Healthcare Big Data

“…A few of the well-known BDA techniques used in the areas of healthcare are described in Table 5. e categories provided in Table 5 are drawn from the literature [12,[66][67][68].…”

“…e categories that are generated in Table 5 are taken from the literature [12,[66][67][68]86]. Big data plays a significant role across all domains such as government organizations, trade associations, healthcare industries, education, and research and development.…”

Big Data Based Diabetes and Heart Disease Prediction System by Employing Supervised Learning Algorithm

“…In [5], big data visualization in cardiology domain is reviewed and the future research directions are discussed. A security model to preserve the medical big data privacy in healthcare cloud is presented on the basis of pairing based cryptography is presented in [6].…”

“…This idea is imposed for choosing the cluster centre point, and the proposed clustering algorithm is presented as follows. The success probability is computed by equation (5), which is as follows.…”