alexa Big Data Analytics in Heart Attack Prediction
ISSN: 2167-1168

Journal of Nursing & Care
Open Access

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Research Article

Big Data Analytics in Heart Attack Prediction

Cheryl Ann Alexander1 and Lidong Wang2*

1Department of Nursing, University of Phoenix, USA

2Department of Engineering Technology, Mississippi Valley State University, USA

*Corresponding Author:
Lidong Wang
Department of Engineering Technology
Mississippi Valley State University, USA
Tel: (+1) 901-515-8006
E-mail: [email protected]

Received date: March 14, 2017; Accepted date: April 21, 2017; Published date: April 29, 2017

Citation: Alexander CA, Wang L (2017) Big Data Analytics in Heart Attack Prediction. J Nurs Care 6:393. doi:10.4172/2167-1168.1000393

Copyright: © 2017 Alexander CA, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are crediteds

 

Abstract

Introduction: Acute myocardial infarction (heart attack) is one of the deadliest diseases patients face. The key to cardiovascular disease management is to evaluate large scores of datasets, compare and mine for information that can be used to predict, prevent, manage and treat chronic diseases such as heart attacks. Big Data analytics, known in the corporate world for its valuable use in controlling, contrasting and managing large datasets can be applied with much success to the prediction, prevention, management and treatment of cardiovascular disease. Data mining, visualization and Hadoop are technologies or tools of big data in mining the voluminous datasets for information. Aim: The aim of this literature review was to identify usage of Big Data analytics in heart attack prediction and prevention, the use of technologies applicable to big data, privacy concerns for the patient, and challenges and future trends as well as suggestions for further use of these technologies. Methods: The national and international databases were examined to identify studies conducted about big data analytics in healthcare, heart attack prediction and prevention, technologies used in big data, and privacy concerns. A total of 31 studies that fit these criteria were assessed. Results: Per the studies analyzed, Big Data analytics is useful in predicting heart attack, and the technologies used in Big Data are extremely vital to the management and tailoring of treatment for cardiovascular disease. And as the use of Big Data in healthcare increases, more useful personalized medicine will be available to individual patients. Conclusion: This review offers the latest information on Big Data analytics in healthcare, predicting heart attack, and tailoring medical treatment to the individual. The results will guide providers, healthcare organizations, nurses, and other treatment providers in using Big Data technologies to predict and manage heart attack as well as what privacy concerns face the use of Big Data analytics in healthcare. Effective and tailored medical treatment can be developed using these technologies.

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