Big Data Analytics in Identification, Treatment, and Cost-Reduction of Hypertension

Cheryl Ann Alexander¹ and Lidong Wang^2,

¹Department of Nursing, University of Phoenix, Tempe, AZ, USA

²Department of Engineering Technology, Mississippi Valley State University, Itta Bena, MS, USA

Cite this paper:
Cheryl Ann Alexander and Lidong Wang. Big Data Analytics in Identification, Treatment, and Cost-Reduction of Hypertension. American Journal of Hypertension Research. 2017; 4(1):1-8. doi: 10.12691/ajhr-4-1-1

Abstract

Hypertension is known as a “silent killer” because patients rarely know they have this deadly disease. Diagnosis is often difficult and can be influenced by factors such as “white coat hypertension” and comorbidities. Most treatment plans consist of modified lifestyles and behavior, and medication when necessary, however many patients do not take medications as directed or consistently. Big Data analytics is the newest method of data processing and management systems for healthcare. It helps determine the most effective treatments, identify patients at risk for hypertension (HTN), suggest treatment plans, and even predict the disease. For providers and patients with high blood pressure, Big Data analytics can be a useful tool for managing data and preventing serious comorbidities and mortality. In this paper, we discuss Big Data analytical tools, HTN, and the use of Big Data in healthcare and HTN.

Keywords:
Big Data analytics hypertension privacy health care electronic medical record (EHR) data mining machine learning

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