Development of a mobile-based hypertension risk monitoring system

Автор: Ngozi C. Egejuru, Oluwadare Ogunlade, Peter A. Idowu

Журнал: International Journal of Information Engineering and Electronic Business @ijieeb

Статья в выпуске: 4 vol.11, 2019 года.

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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.

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Classification, Diagnosis, Hypertension, Mobile-based, Monitoring, Risk

Короткий адрес: https://sciup.org/15016180

IDR: 15016180   |   DOI: 10.5815/ijieeb.2019.04.02

Текст научной статьи Development of a mobile-based hypertension risk monitoring system

Published Online July 2019 in MECS

Uncontrolled hypertension is an ongoing challenge to health care systems worldwide [1]. When hypertension is poorly controlled, it increases mortality, morbidity, and economic burden especially among older adults, and it is a major public health concern all over the world, [2].

Hypertension or high blood pressure (HBP) is a cardiac chronic medical condition in which the systemic arterial blood pressure is elevated [3]. Hypertension condition is categorized as either primary (essential) or secondary hypertension. Primary hypertension does not have a clear 1 medical cause, while secondary hypertension is linked to identifiable causes such as vascular disorder, kidney diseases or endocrine [4,5].

Hypertension is diagnosed with persistent elevation of the systolic blood pressure (SBP) >  140 mmHg and diastolic blood pressure (DBP) >  90 mmHg based on the average of two or more correct blood pressure measurement taken two or more contact with healthcare providers [6]. Globally, the overall prevalence of hypertension in adults from age 25 and over, was around 40% in 2008 [7].

It is vital to control illness, which involves carrying out necessary investigations, essential enquiries into the patient history and the administration of therapy by the Doctor (medical practitioner). The essence of this, is to ensure that the patients BP remain within the normal range [8]. Hypertension is the leading and most essential modifiable risk factor for heart diseases, renal diseases, stroke, and retinopathy [9].

In Sub-Saharan Africa, studies have shown that uncontrolled hypertension contributed to stroke, chronic kidney disease, retina (sight) problem, aortic dissection and heart failure [4]. Surveys in some countries on blood pressure (BP) showed that less than 25% of patients with hypertension are under good BP control [1].

According to Logan et al. [10], concern over the rise in a poor level of blood pressure (BP) control among patients with hypertensive, has led to serious early search for new ways of managing hypertension. Also, patients are willing to become more actively involved in managing their own healthcare [11]. Introduction of selfmonitoring whether at home or office, in short, any location becomes a way of increasing patient’s involvement in managing hypertension.

A disease risk monitoring includes the automation of the risk assessment of diseases, which provides cost savings for the patients/hospitals management, and increases the efficiency of the specialists [12,13,14]. Using information and communication technology (ICT), which involves mobile computing, distributed computing, and the internet, can provide healthcare service to monitor hypertension and its risk anytime, and anywhere.

Mobile technology is an ubiquitous tool in managing everyday life over a variety of applications. It can be used by an intending user to provide quality healthcare service from any remote location and is very portable thereby making services easily accessible and available. Mobile technology can assist in developing a system that has a communication platform for patients and doctors. With the use of mobile technologies, hypertension risk can be easily identified and managed by doctors and patients. Patients will be directed to conduct necessary investigations to know their health status.

The need to develop and organize new ways of providing effective and efficient healthcare service has increased with the use of Information Technology [15,16]. The use of IT in healthcare service (e-Health), involves using communication technologies, such as the internet, computer systems, portable, wireless and other devices in support of healthcare delivery and education [17,18]. eHealth entails a fundamental redesign of healthcare processes based on the use and integration of electronic communications in all levels. The medical information of a patient can be stored electronically, and be used in decision making concerning the patient’s treatment and health. It will help in providing reliable, flexible, timely and secure healthcare delivery to patients by the medical practitioners.

The integration of clinical decision support system might decrease medical errors, enhance the patient result, decrease unwelcome practice disparity, and improve patient's protection [19]. The integration of computing platforms and wireless communication technologies in healthcare systems has enhanced the quality of health service for millions of people all over the world [20]. The use of ICT has brought revolution to healthcare services and using mobile technology will provide the ability to instantly update patient’s records, which will help the medical practitioners to make the more accurate decision and enhance quality care for patients, which will also help in early detection, diagnosis, and management of patient's health.

There is a need to design the processes that are centred on the need of patients [21] and elicit knowledge from the experts on the processes involved. This will enable patients to easily access the required information needed in taking care of their own health. Medical decision support systems are becoming more and more essential in assisting the doctors to take correct decisions [22]. There is a need for the development of a mobile-based system for the classification of the risk of hypertension. This will enhance early detection of the disease for providing clinical decision support and improving the living standard of the patients.

The aim of this study is to build a mobile-based classification system called BP_HRMSystem that can identify or diagnose individuals suffering from hypertension, classify and manage the hypertension risk, which will greatly help in creating awareness.

This system has a communication platform that can allow the doctor and patient to communicate with each other through the administrator. The patient can leave messages for the doctor and get related advice from the doctor. Then, medical practitioners can check the messages and give related answers or replies. Moreover, the medical practitioner can give some advice when they view the current condition of the patient. This will help the patient to take the right steps or treatment required.

The system has a knowledgeable database that can be used to guide patients and medical practitioners. The system keeps track of the patient's medical history and is able to plot a graph on the blood pressure of the patient. The patient and the medical practitioner can see the way the blood pressure is fluctuating, evaluate the patient and make decisions. The system keeps track of the body mass index (BMI) of the patient, which can be seen at a glance.

  • II.    Related Works

Список литературы Development of a mobile-based hypertension risk monitoring system

  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • World Health Organisation (2011). WHO Maps: Non-Communicable Disease Trend In All Countries. World Health Global Report, World Health Organisation.
  • 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.
  • 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.
  • 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.
  • 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
  • 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.
  • 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.
  • 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.
  • 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.
  • 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
  • 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
  • Gunther Eysenbach (2001). “What is e-Health?”, Journal of Medical Internet Research, 2001.
  • 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
  • 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.
  • 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.
  • WHO, “Strategy 2004-2007: eHealth for Health-care Delivery”. www.who.int/eht/en/eHealth_HCD.pdf
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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
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