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     Research Journal of Applied Sciences, Engineering and Technology


Hypertension Interventions using Classification Based Data Mining

Abdullah A. Aljumah and Mohammad Khubeb Siddiqui
College of Computer Engineering and Sciences, Salman Bin Abdulaziz University, Saudi Arabia
Research Journal of Applied Sciences, Engineering and Technology  2014  17:3593-3602
http://dx.doi.org/10.19026/rjaset.7.712  |  © The Author(s) 2014
Received: November 13, 2013  |  Accepted: November 25, 2013  |  Published: May 05, 2014

Abstract

In the present study, we would like to gain the insight of the medical data through classification based data mining technique. The data sets of NCD (Non Communicable Diseases) risk factors, a standard report of Saudi Arabia 2005, in collaboration with WHO (World Health Organisation, 2005) were employed on Saudi hypertension patients. Computing the probability and prediction of hypertension disease intervention are evaluated through ROC (Receiver Operating Characteristics). The Area under Curve (AUC) of depicted ROC plots are calculated, the AUC of ROC is the indicative of prediction of the intervention. The AUC of ROC of five hypertension interventions is obtained based on which we distinguish which mode of intervention is more appropriate. Present analysis predicts that smoking cessation is the best intervention followed by exercise, diet, weight and drug for the hypertension intervention in Saudi Arabia.

Keywords:

Classification, data mining, hypertension, Na, Receiver Operating Characteristics (ROC),


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Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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