IJIEEB Vol. 11, No. 4, 8 Jul. 2019
Cover page and Table of Contents: PDF (size: 1615KB)
Full Text (PDF, 1615KB), PP.11-23
Views: 0 Downloads: 0
Classification, Diagnosis, Hypertension, Mobile-based, Monitoring, Risk
Hypertension is a silent killer, which gives no warning signs to alert a patient and can only be detected through regular blood pressure check¬ups. Uncontrolled and unmonitored hypertension contributed to stroke, chronic kidney disease, eye problem, and heart failure. It is an ongoing challenge to health care systems worldwide. Early detection of hypertension and creating awareness will greatly reduce the effect of hypertension and its related diseases. Also, having a mobile-based system will help patients to know their status, relate with Doctor and enjoy the quick response from the Doctor on hypertension diagnostic effect on their health. The mobile application will help in monitoring patients anytime, anywhere and provide services for each patient based on their personal health condition. The mobile application was designed using unified modeling language and implemented using the Extensible Mark-Up Language and Java programming language for the mobile layout and content, while JavaScript Object Notation was used to implement the data storage and retrieval mechanism of the system. The system was tested using data collected from hospital, which yielded an accuracy of 100%. In conclusion, the system will assist in providing timely, efficient, accurate and comprehensive information about hypertension, which is useful for Doctors and patients in detecting, diagnosing, classifying and managing hypertension and its risk.
Ngozi C. Egejuru, Oluwadare Ogunlade, Peter A. Idowu, "Development of a Mobile-Based Hypertension Risk Monitoring System", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.11, No.4, pp. 11-23, 2019. DOI:10.5815/ijieeb.2019.04.02
[1]K. Wolf-Maier, R. S. Cooper, H. Kramer, J. R Banegas, S. Giampaoli, M. R. Joffres, et al. (2004). Hypertension treatment and control in five European countries, Canada, and the United States. Hypertension; 43: 10–17.
[2]G. Ogedegbe, S. Fernandez, L. Fournier, S. A. Silver, J. Kong, S. Gallagher, et al. (2013). The Counseling Older Adults to Control Hypertension (COACH) trial: Design and Methodology of a Group-based Lifestyle Intervention for Hypertensive Minority Older Adults. Contemporary Clinical Trials 35(1): 70 - 79.
[3]X. Y. Djam and Y. H. Kimbi (2011). Fuzzy Expert System for the Management of Hypertension. The Pacific Journal of Science and Technology 12(1): 390 – 402.
[4]A. A. Imianvan and J.C. Obi (2012). Cognitive Neuro-Fuzzy Expert System for Hypotension Control. Computer Engineering and Intelligent Systems 3(6): 21 – 31.
[5]K. Obahiagbon and B. B. Odigie. (2015). A Framework for Intelligent Remote Blood Pressure Monitoring and Control System for Developing Countries. Journal of Computer Sciences and Applications 3(1): 11 – 17.
[6]World Health Organisation (2011). WHO Maps: Non-Communicable Disease Trend In All Countries. World Health Global Report, World Health Organisation.
[7]World Health Organisation (2015). Global Health Observatory (GHO) Data: Raised Blood Pressure. Available from http://www.who.int/gho/ncd/risk_factors/blood_pressure _prevalence_text/en/ on June 25, 2016.
[8]A. V. Chobanian, G. L. Bakris, H. R. Black, W. C. Cushman, L. A. Green., J. L. Izzo, et al. (2003). The 7th Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: The JNC 7 report. Journal of the American Medical Association 289: 2560 - 2672.
[9]I. A. Bani (2011). Prevalence and Related Risk Factors of Essential Hypertension in Jazan Region, Saudi Arabia. Sudanese Journal of Public Health 6(2): 45-50.
[10]A. G. Logan, W. J. McIsaac, A. Tisler, M. J. Irvine,., A. Saunders, A. Dunai, et al. (2007). American Journal of Hypertension, 20(9): 942–948, https://doi.org/10.1016/j.amjhyper.2007.03.020
[11]K. R. Lorig, D. S. Sobel, P. L. Ritter, D. Laurent, and M. Hobbs (2001). Effect of a self-management program in patients with chronic disease. E_ Clin Pract; 4: 256–262.
[12]P. Srivastava, and A. Srivastava (2012). Spectrum of Soft Computing Risk Assessment Scheme for Hypertension. In International Journal of Computer Applications. 44(17): 23 – 30.
[13]P. Srivastava, A. Srivastava, A, Burande, and A. Khandelwal (2013). A Note on Hypertension Classification Scheme and Soft Computing Decision Making System. ISRN Biomathematics: 1 - 11.
[14]A. Kaur and A. Bhardwaj (2014). Genetic Neuro-Fuzzy System for Hypertension Diagnosis. International Journal of Computer Science and Information Technologies 5(4): 4986 – 4989.
[15]H. Joseph and J. Tan (2002). “The Evolving Face of Telemedicine and e-Health: Opening Doors and Closing Gap’s in E-Health Services Opportunities and Challenges”, Proceedings of the 36th Hawaii International Conference on System Sciences (HICSS’03), IEEE, 2002
[16]Jen-Her Wu, Shu-Ching Wang, and Li-Min Lin (2005). “What Drives Health Care? An Empirical Evaluation of Technology Acceptance”, Proceedings of 38th Hawaii International Conference on System Sciences, IEEE, 2005
[17]Gunther Eysenbach (2001). “What is e-Health?”, Journal of Medical Internet Research, 2001.
[18]R. Jones, R. Rogers, J. Roberts, L. Callaghan, L. Lindsey, J. Campbell, et al. (2005)., “What Is eHealth (5): A Research Agenda for eHealth Through Stakeholder Consultation and Policy Context Review” Journal of Medical Internet Research, Vol 7, Issue 5, 2005
[19]J. Cheng and R. Greiner (1999). Comparing Bayesian Network Classifiers. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann Publishers Inc., Alberta, Canada: 101 - 108.
[20]Z. Benyó, P. Várady, B. Benyó and B. Tóth (1999). Remote Patient Monitoring System Based on an Industry Standard Fieldbus. In 2nd World Congress on Biomedical Communication, Amsterdam: 5 – 8.
[21]WHO, “Strategy 2004-2007: eHealth for Health-care Delivery”. www.who.int/eht/en/eHealth_HCD.pdf
[22]N. C. Egejuru, P. D. Mhambe, J. A. Balogun,., F. Komolafe, and P. A. Idowu (2017). Osteoporosis Risk Predictive Model Using Supervised Machine Learning Algorithms. Science Publishing Group, Engineering and Applied Science.
[23]J. Chalmers, S. MacMahon, G. Mancia, J. Whitworth, L. Beilin, L. Hansson, et al. (1999). 1999 World Health Organisation-International Society of Hypertension Guidelines for the management of hypertension. Guidelines sub-committee of the World Health Organisation. Clinical and experimental hypertension. New York: USA: 1009 - 1060.
[24]A. Bolaji (2014). Simulation of a Real-Time Mobile Health Monitoring System Model for Hypertensive Patients in Rural Nigeria. African Journal of Computing and ICT 7(1): 95 – 100.
[25]J. O. Egwaile, O. I. Omoifo, O. O. Odia, and O. Okosun (2016). Development of a Real Time blood pressure, temperature measurement and reporting system for in-patients. International Journal of Physical Sciences 11(17), 2016, 225 – 232.
[26]P. A. Idowu, S. O. Ajibola, and J. A. Balogun, Development of a web based Cardiovascular Disease Risk Monitoring System. Ife Journal of Information Communication Technology 1(1), 2016, 4 - 16.
[27]A. D. Lopez, D. Andrea and A. R. Carlos (2006). Global and regional burden of disease and risk factors: Systematic analysis of population health data. Lancet 367(9524): 1747 – 1757.
[28]B. Ordinioha, (2016). The prevalence of hypertension and its modifiable risk factors among lecturers of a medical school in Port Harcourt, south-south Nigeria: implications for control effort. Nigerian Journal of Clinical Practice 16(1): 1 – 11