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Abstract
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Article Information:
Hypertension Interventions using Classification Based Data Mining
Abdullah A. Aljumah and Mohammad Khubeb Siddiqui
Corresponding Author: Mohammad Khubeb Siddiqui
Submitted: November 13, 2013
Accepted: November 25, 2013
Published: May 05, 2014 |
Abstract:
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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.
Key words: Classification, data mining, hypertension, Naïve Bayesian algorithm, Receiver Operating Characteristics (ROC), ,
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Cite this Reference:
Abdullah A. Aljumah and Mohammad Khubeb Siddiqui, . Hypertension Interventions using Classification Based Data Mining. Research Journal of Applied Sciences, Engineering and Technology, (17): 3593-3602.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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