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General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2015 Vol. 5(1): 73-77 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2015.V5.486

Investigating Gender Effect on Traditional and Herbal Remedies to Manage Diabetes in KSA

Yahya M. AlMurtadha, Osman A. Abdalla, Abdelrahman O. Elfaki, and Mustafa H. Alfrraj
Abstract—Consulting Traditional healers and use herbs for managing diabetes are common in Saudi Arabia despite the major development in health care systems. This study aims at investigating the gender effect on managing diabetes for those consult traditional healers and those use herbs to raise the alarm for the medical policy makers of the reasonable percentage of patients following such traditional remedies. The research used the Saudi Arabia NCD2005 obtained from WHO site the data reported as a result of the national survey of non-communicable disease in 2005. The investigation will start with statistical analysis followed by a predictive analysis of gender factor for those managing diabetes by consulting traditional healers or using herbs using regression analysis (support vector machine) as a data mining technique. Rapidminer was used as a data mining software for predicting which gender group (Men or Women) most attracted to those kinds of traditional remedies. The findings show that gender is a significant factor helps to predict the traditional remedy type. The results highlight that women are more affected by consulting traditional healers than men while men are better in managing diabetes by using herbs. Though, it is recommended for the medical policy makers to encourage the medical doctors to discuss with their patients if they are using such traditional remedies which may affect the medical therapy. Future work includes comparing the effectiveness of managing diabetes in Saudi Arabia by modern health care medication against using herbs.

Index Terms—Data mining, diabetes traditional healers, diabetes herbs remedy, gender and diabetes, diabetes in KSA.

Yahya M. AlMurtadha and Abdelrahman O. Elfaki are with the Department of Computer Science, Faculty of Computers and Information Technology, University of Tabuk, Tabuk, Kingdom of Saudi Arabia (e-mail: y.murtadha@ut.edu.sa, a.elfaki@ut.edu.sa).
Osman A. abdalla is with the Department of Information Technology, Faculty of Computers and Information Technology, University of Tabuk. Kingdom of Saudi Arabia (e-mail: osman_uofg@ut.edu.sa).
Mustafa H. Alfrraj is with the Diabetes Center, King Khalid Hospital, Tabuk, Kingdom of Saudi Arabia (e-mail: dr_mh2009@hotmail.com).

[PDF]

Cite: Yahya M. AlMurtadha, Osman A. Abdalla, Abdelrahman O. Elfaki, and Mustafa H. Alfrraj, "Investigating Gender Effect on Traditional and Herbal Remedies to Manage Diabetes in KSA," International Journal of Machine Learning and Computing vol. 5, no. 1, pp. 73-77, 2015.

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