Abstract   In the present era, the data mining techniques are widely and deeply useful as decision support system s in the fields of health care systems. The proposed research is an interdisciplinary work of informatics and health care, with the help of data mining techniques to predict the relationship among interventions of hypertension and diabetes. As the study shows persons who have diabetes can have chances of hypertension and vice versa. In the present work we would like to approach the life style intervention of hypertension and diabetes and their effects using data mining. Life style intervention plays a vital role to control these diseases. The intervention includes t he risk factor like diet, weight, smoking cessation and exercise. The regression technique is used in which dependent and Independent Variables (IV) are defined. The four interventions are treated as (IV) and two diseases hypertension and d iabetes are Dependent Variables (DV). We have estab lished the relationship between hypertension and diabetes, usi ng the data set of Non Communicable Disease (NCD) r eport of Saudi Arabia, World Health Organisation’s (WHO). The Orac le Data Miner (ODM) tool is used to analyse the data set. Predictive data analysis gives the result that interventions w eight control and exercise have the direct relation ship between them in both the diseases.  

References

[1] Aljumah A., Ahamad M., and Siddiqui M., Application of Data Mining: Diabetes Health Care in Young and Old Patients, Journal of King Saud University@Computer and Information Sciences , vol. 25, no. 2, pp. 127-136, 2013.

[2] Aljumah A., Siddiqui M., and Ahamad M., Application of Classification based Data Mining Technique in Diabetes Care, Journal of Applied Sciences , vol. 13, no. 3, pp. 416-422, 2013.

[3] Almazyad A., Ahamad M., Siddiqui M., and Almazyad A., Effective Hypertensive Intervention using Data Mining in Saudi Arabia, Journal of Clinical Monitoring and Computing , vol. 24, no. 6, pp. 391-401, 2010.

[4] Al-Nozha M., Al-Maatouq M., Al-Mazrou Y., Al-Harthi S., Arafah M., Khalil M., Khan N., Al- Khadra A., Al-Marzouki K., Nouh M., Abdullah M., Attas O., Al-Shahid M., and Al-Mobeireek A., Diabetes Mellitus in Saudi Arabia, Saudi Medical Journal , vol. 25, no. 11, pp. 1603-1613, 2004.

[5] Al-Nozha M., Abdullah M., Arafah M., Khalil M., Khan N., Al-Mazrou Y., Al-Maatouq M., Al- Marzouki K., Al-Khadra A., Nouh M., Al-Harthi S., Al-Shahid M., and Al-Mobeireek A., Hypertension in Saudi Arabia, Saudi Medical Journal , vol. 28, no. 1, pp. 77-84, 2007.

[6] Appel L., Brands M., Daniels S., Karanja N., Elmer P., and Sacks F., Dietary Approaches to Prevent and Treat Hypertension: A Scientific Statement from the American Heart Association, Hypertension, vol. 47, no. 2, pp. 296-308, 2006.

[7] Breault J., Goodall C., and Fose P., Data Mining a Diabetic Data Warehouse, Artificial Intelligence in Medicine , vol. 26, no. 1-2, pp. 37- 54, 2002.

[8] Chang C., Wang C., and Jiang B., Using Data Mining Techniques for Multidiseases Prediction Modeling of Hypertension and Hyperlipidemia by Common Risk Factors, Expert Systems with Applications , vol. 38, no. 5, pp. 5507-5513, 2011.

[9] Diabetes and High Blood Pressure, available at: http://www.patient.co.uk/health/diabetes-and- high-blood-pressure, last visited 2014.

[10] Dickinson H., Mason J., Nicolson D., Campbell F., Beyer F., Cook S., Williams B., and Ford G., Lifestyle Interventions to Reduce Raised Blood Pressure: A Systematic Review of Randomised Controlled Trials, Journal of Hypertens , vol. 24, no. 2, pp. 215-233, 2006.

[11] Epstein M. and Sowers J., Diabetes Mellitus and Hypertension, Hypertension, vol. 19, pp. 403- 418, 1992.

[12] Georga E., Protopappas V., Bellos C., Makriyiannis D., and Fotiadis D., Interpretation of Long-Term Clinical Diabetes Data and Prediction of Glycemic Control based on Data Mining Technique, in Proceedings of the International Conference on Health Informatics , Vilamoura, pp. 315-318 2014.

[13] Han J., Rodriguez J., and Beheshti M., Diabetes Data Analysis and Prediction Model Discovery using RapidMiner, in Proceedings of the 2 nd International Conference on Future Generation Communication and Networking , Hainan Island, pp. 96-99, 2008.

[14] Krishna B. and Kaliaperumal B., Efficient Genetic-Wrapper Algorithm based Data Mining for Feature Subset Selection in a Power Quality Pattern Recognition Application, the International Arab Journal of Information Technology , vol. 8, no. 4, pp. 397-405, 2011.

[15] Mancia G., DeBacker G., Dominiczak A., Cifkova R., Fagard R., and Germano G., ESH- ESC Practice Guidelines for the Management of Arterial Hypertension: ESH-ESC Task Force on the Management of Arterial Hypertension, Journal Hypertens , vol. 25, no. 9, pp. 1751-1762, 2007.

[16] Rajesh K. and Sangeetha V., Application of Data Mining Methods and Techniques for Diabetes Diagnosis, the International Journal of Engineering and Innovative Technology , vol. 2, no. 3, pp. 224-229, 2012.

[17] Richards G., Rayward-Smith V., Sonksen P., Carey S., and Weng C., Data Mining For Indicators of Early Mortality in A Database of Clinical Records, Artificial Intelligence in Medicine , vol. 22, no. 3, pp. 215-231, 2001.

[18] Sa-Ngasoongsong A. and Chongwatpol J., An Analysis of Diabetes Risk Factors using Data Mining Approach, available at: Data Mining Perspective: Prognosis of Life Style on Hypertension and Diabetes 99 http://www.lexjansen.com/mwsug/2012/PH/MW SUG-2012-PH10.pdf, last visited 2012.

[19] Tapak L., Mahjub H., Hamidi O., and Poorolajal J., Real-Data Comparison of Data Mining Methods in Prediction of Diabetes in Iran, Healthc Inform Res , vol. 19, no. 3, pp. 177-85, 2013.

[20] Vapnik V., The Nature of Statistical Learning Theory , Springer-Verlag, New York, USA, 1995.

[21] Winslow E., Bohannon N., Brunton S., and Mayhew H., Lifestyle Modification: Weight Control, Exercise and Smoking Cessation, Am J Med , vol. 101, no. 4, pp. 25-31, 1996.

[22] Who Country Cooperation Strategy for Saudi Arabia., available at: http://www.who.int/countryfocus/cooperation_str ategy/ccs_sau_en.pdf, last visited 2011.

[23] World Health Organisation, NCD risk factor, standard report of Ministry of Health, Saudi Arabia., available at: http://www.who.int/chp/steps/2005_SaudiArabia _STEPS_Report_EN.pdf, last visited 2005 Abdullah Aljumah received PhD in electronic engineering from University of Wales, UK. His main area of research is artificial intelligence, digital design and data mining. Currently, he is working as an associate professor as well as Dean of the College of Computer Engineering and Sciences and Vice Rector of Salman Bin Abdulaziz University, Saudi Arabia. He is also, a consultant for several Government Organizations and a member of councils of various boards and commissions. Aljumah has published a number of research papers in repute d Conferences and Journals. Mohammad Siddiqui received his BTech in computer science engineering from Uttar Pradesh Technical University, India and MS from BITS, India. His research field is databases, data warehousing and application of data mining. Currently, he is working as a Researcher in College of Computer Engineering and Sciences, Salman bin Abdulaziz University, Saudi Arabia. He worked as an Oracle DBA for various telecom based project. he ha d done funded projects by Deanship of Scientific Research, Salman bin Abdulaiz University, Ministry of Higher Education, Saudi Arabia. He has published a number of research papers in reputed Conferences an d Journals. He is reviewer of various reputed Journal .