Determining Significant Risk Factors for Preventing Elderly People with Hypertension from Cardiovascular Disease Complication Using Maximum Objective Distance

Abstract

With the increasing aging population worldwide, cardiovascular disease (CVD) has become the leading cause of disability and death. Hypertension is one of the most critical factors causing CVD complication. Determining risk factors of hypertension is extremely important for preventing elderly people with hypertension from CVD complication. Accordingly, this study proposes a new measurement so-called objective distance that is influenced by the significant risk factors of hypertension development. The assumption is made that the maximum objective distance is derived from significant risk factors. This study employs the secondary data of 121 elderly people aged 65 and over, derived from hospitals in Chiang Rai, Thailand. The gathered data contains all potential risk factors of hypertension development, including total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), physical activity, smoking, medication adherence, and body mass index. The decision tree is employed in this study for determining the significant risk factors in order to verify the proposed assumption. The results show that the obtained significant risk factors are TC, LDL-C, HDL-C, physical activity, and smoking. Importantly, the combination of obtained significant risk factors provides the maximum objective distance regarding the proposed assumption.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3

References

  1. 1.

    United Nations, Department of Economic and Social Affairs, Population Division. (2017). World population ageing 2017—Highlights (ST/ESA/SER.A/397). New York: United Nations, Department of Economic and Social Affairs, Population Division. Retrieved February 13, 2019 from https://www.un.org/en/development/desa/population/publications/pdf/ageing/WPA2017_Highlights.pdf.

  2. 2.

    World Health Organization. (2016). Cardiovascular disease (CVDs) fact sheet. Geneva: WHO.

    Google Scholar 

  3. 3.

    World Health Organization Thailand. (2017). New tools launched to tackle cardiovascular disease, the biggest killer in Thailand. Nonthaburi: World Health Organization Thailand. Retrieved February 13, 2019 from http://origin.searo.who.int/thailand/press-release-world-heart-day-2017-english.pdf.

  4. 4.

    Wu, C. Y., Hu, H. Y., Chou, Y. J., Huang, N., Chou, Y. C., & Li, C. P. (2015). High blood pressure and all-cause and cardiovascular disease mortalities in community-dwelling older adults. Medicine, 94(47), 1–10.

    Google Scholar 

  5. 5.

    National Heart Foundation of Australia. (2016).Guideline for the diagnosis and management of hypertension in adults-2016. Retrieved February 13, 2019, from https://www.heartfoundation.org.au/images/uploads/publications/PRO-167_Hypertension-guideline-2016_WEB.pdf.

  6. 6.

    Mancia, G., Fagard, R., Narkiewicz, K., Redon, J., Zanchetti, A., et al. (2013). 2013 ESH/ESC guidelines for the management of arterial hypertension: The task force for the management of arterial hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). European Heart Journal, 34, 2159–2219.

    Google Scholar 

  7. 7.

    Ferguson, T. S., Younger-Coleman, N. O., Tulloch-Reid, M. K., Bennett, N. R., Rousseau, A. E., Knight-Madden, J. M., et al. (2018). Factors associated with elevated blood pressure or hypertension in Afro-Caribbean youth: A cross-sectional study. PeerJ, 6, e4385.

    Google Scholar 

  8. 8.

    World Heart Federation. (2017). Stroke and hypertension. Retrieved February 13, 2019, from https://www.world-heart-federation.org/resources/stroke-and-hypertension/.

  9. 9.

    World Health Organization. (2019). Hypertension. Retrieved February 12, 2019, from https://www.who.int/topics/hypertension/en/.

  10. 10.

    Qamar, A., & Braunwald, E. (2018). Treatment of hypertension: Addressing a global health problem. JAMA, 320(17), 1751–1752. https://doi.org/10.1001/jama.2018.16579.

    Article  Google Scholar 

  11. 11.

    Mendis, S. (2010). Global status report on non communicable diseases 2010. Technical report, World Health Organisation, 2010. Retrieved February 15, 2019, from http://www.who.int/nmh/publications/ncd_report2010/en/.

  12. 12.

    Tabrizi, J. S., Sadeghi-Bazargani, H., Farahbakhsh, M., Nikniaz, L., & Nikniaz, Z. (2016). Prevalence and associated factors of prehypertension and hypertension in Iranian population: The Lifestyle Promotion Project (LPP). PLoS ONE, 11(10), e0165264.

    Google Scholar 

  13. 13.

    Devadason, P., Sabarinath, M., Reshma Dass, R., Sameena, A., Sanjeetha Fathima, S., Mathiarasu, A. M., et al. (2014). Risk factors for hypertension and its complications—A hospital based case control study. International Journal of Interdisciplinary and Multidisciplinary Studies (IJIMS), 1(4), 160–163.

    Google Scholar 

  14. 14.

    Sen, A., Das, M., Basu, S., & Datta, G. (2015). Prevalence of hypertension and its associated risk factors among Kolkata-based policemen: A sociophysiological study. International Journal of Medical Science and Public Health, 4, 225–232.

    Google Scholar 

  15. 15.

    Wang, F., Tiwari, V. K., & Wang, H. (2014). Risk factors for hypertension in india and china: A comparative study. Health and Population: Perspectives and Issues, 37(1), 40–49.

    Google Scholar 

  16. 16.

    Kjeldsen, S., Feldman, R. D., Lisheng, L., Mourad, J. J., Chiang, C. E., Zhang, W., et al. (2014). Updated national and international hypertension guidelines: A review of current recommendations. Drugs, 74(17), 2033–2051.

    Google Scholar 

  17. 17.

    Halter, J. B., Musi, N., Horne, F. M., Crandall, J. P., Goldberg, A., Harkless, L., et al. (2014). Diabetes and cardiovascular disease in older adults: Current status and future directions. Diabetes, 63(8), 2578–2589.

    Google Scholar 

  18. 18.

    Rathod, A. B., Gulhane, S. M., & Padalwar, S. R. (2016). A comparative study on distance measuring approches for permutation representations. In 2016 IEEE international conference on advances in electronics, communication and computer technology (ICAECCT) (pp. 251–255).

  19. 19.

    Chaichumpa, S., & Temdee, P. (2018). Assessment of student competency for personalised online learning using objective distance. International Journal of Innovation and Learning, 23(1), 19–36.

    Google Scholar 

  20. 20.

    Chaising, S., & Temdee, P. (2018). Determining recommendations for preventing elderly people from cardiovascular disease complication using objective distance. In The 6th global wireless summit (GWS-2018).

  21. 21.

    National Heart, Lung, and Blood Institute. (2005). Your guide to lowering your cholesterol with TLC. Bethesda, MD: US Department of Health and Human Service.

    Google Scholar 

  22. 22.

    Dua, S., Bhuker, M., Sharma, P., Dhall, M., & Kapoor, S. (2014). Body mass index relates to blood pressure among adults. North American Journal of Medical Sciences, 6(2), 89.

    Google Scholar 

  23. 23.

    Hacıhasanoğlu Aşılar, R. (2015). Medication adherence and self-care management in hypertension. Turkish Journal of Cardiovascular Nursing, 6(11), 151–159.

    Google Scholar 

  24. 24.

    Yang, Q., Chang, A., Ritchey, M. D., & Loustalot, F. (2017). Antihypertensive medication adherence and risk of cardiovascular disease among older adults: A population-based cohort study. Journal of the American Heart Association, 6(6), e006056.

    Google Scholar 

  25. 25.

    Mahmuda, S., Yeasmin, N., Abira, M., Rahman, F., Hasan, M., Rabbani, R., et al. (2018). Association of serum low density lipoprotein cholesterol and high density lipoprotein cholesterol with hypertension in adult female. Bangladesh Critical Care Journal, 6(2), 74–79. https://doi.org/10.3329/bccj.v6i2.38581.

    Article  Google Scholar 

  26. 26.

    Ferrara, L. A., Guida, L., Iannuzzi, R., Celentano, A., & Lionello, F. (2002). Serum cholesterol affects blood pressure regulation. Journal of Human Hypertension, 16(5), 337.

    Google Scholar 

  27. 27.

    Sesso, H. D., Buring, J. E., Chown, M. J., Ridker, P. M., & Gaziano, J. M. (2005). A prospective study of plasma lipid levels and hypertension in women. Archives of Internal Medicine, 165(20), 2420–2427.

    Google Scholar 

  28. 28.

    Egan, B. M., Li, J., Qanungo, S., & Wolfman, T. E. (2013). Blood pressure and cholesterol control in hypertensive hypercholesterolemic patients: National health and nutrition examination surveys 1988–2010. Circulation, 128(1), 29–41.

    Google Scholar 

  29. 29.

    Otsuka, T., Takada, H., Nishiyama, Y., Kodani, E., Saiki, Y., Kato, K., et al. (2016). Dyslipidemia and the risk of developing hypertension in a working-age male population. Journal of the American Heart Association, 5(3), e003053.

    Google Scholar 

  30. 30.

    Diaz, K. M., & Shimbo, D. (2013). Physical activity and the prevention of hypertension. Current Hypertension Reports, 15(6), 659–668.

    Google Scholar 

  31. 31.

    Pescatello, L. S., MacDonald, H. V., Ash, G. I., Lamberti, L. M., Farquhar, W. B., Arena, R., & Johnson, B. T. (2015). Assessing the existing professional exercise recommendations for hypertension: A review and recommendations for future research priorities. In Mayo Clinic proceedings (Vol. 90, No. 6, pp. 801–812). Amsterdam: Elsevier.

  32. 32.

    Sharman, J. E., & Stowasser, M. (2009). Australian association for exercise and sports science position statement on exercise and hypertension. Journal of Science and Medicine in Sport, 12(2), 252–257.

    Google Scholar 

  33. 33.

    Martins, L. C. G., Lopes, M. V. D. O., Guedes, N. G., Nunes, M. M., Diniz, C. M., & Carvalho, P. M. D. O. (2015). Sedentary lifestyle in individuals with hypertension. Revista brasileira de enfermagem, 68(6), 1005–1012.

    Google Scholar 

  34. 34.

    Gandasentana, R. D., & Kusumaratna, R. K. (2016). Physical activity reduced hypertension in the elderly and cost-effective. Universa Medicina, 30(3), 173–181.

    Google Scholar 

  35. 35.

    Ukawa, S., Tamakoshi, A., Wakai, K., Ando, M., & Kawamura, T. (2015). Body mass index is associated with hypertension in Japanese young elderly individuals: Findings of the new integrated suburban seniority investigation. Internal Medicine, 54(24), 3121–3125.

    Google Scholar 

  36. 36.

    Ain, Q. U., & Regmi, K. (2015). The effects of smoking in developing hypertension in Pakistan: A systematic review. South East Asia Journal of Public Health, 5(1), 4–11.

    Google Scholar 

  37. 37.

    Noubiap, J. J., Nansseu, J. R., Endomba, F. T., Ngouo, A., Nkeck, J. R., Nyaga, U. F., et al. (2019). Active smoking among people with diabetes mellitus or hypertension in Africa: A systematic review and meta-analysis. Scientific Reports, 9(1), 588.

    Google Scholar 

  38. 38.

    Li, G., Wang, H., Wang, K., Wang, W., Dong, F., Qian, Y., et al. (2017). The association between smoking and blood pressure in men: A cross-sectional study. BMC Public Health, 17(1), 797.

    Google Scholar 

  39. 39.

    Boratas, S., & Kilic, H. F. (2018). Evaluation of medication adherence in hypertensive patients and influential factors. Pakistan Journal of Medical Sciences, 34(4), 959.

    Google Scholar 

  40. 40.

    Tilea, L., Petra, D., Voidazan, S., Ardeleanu, E., & Varga, A. (2018). Treatment adherence among adult hypertensive patients: A cross-sectional retrospective study in primary care in Romania. Patient Preference and Adherence, 12, 625–635.

    Google Scholar 

  41. 41.

    Ghosh, A., & Barman, S. (2016). Application of Euclidean distance measurement and principal component analysis for gene identification. Gene, 583(2), 112–120.

    Google Scholar 

  42. 42.

    Ivanov, S. E., Gorlushkina, N., & Govorov, A. (2018). The recognition and classification of objects based on the modified distance metric. Procedia Computer Science, 136, 210–217.

    Google Scholar 

  43. 43.

    Kotsiantis, S., Patriarcheas, K., & Xenos, M. (2010). A combinational incremental ensemble of classifiers as a technique for predicting students’ performance in distance education. Knowledge-Based Systems, 23(6), 529–535.

    Google Scholar 

  44. 44.

    Moon, M., & Lee, S. K. (2017). Applying of decision tree analysis to risk factors associated with pressure ulcers in long-term care facilities. Healthcare Informatics Research, 23(1), 43–52.

    Google Scholar 

  45. 45.

    Adebayo, I. P. (2017). Predictive model for the classification of hypertension risk using decision trees algorithm. American Journal of Mathematical and Computer Modelling, 2(2), 48–59.

    Google Scholar 

  46. 46.

    Leach, H. J., O’Connor, D. P., Simpson, R. J., Rifai, H. S., Mama, S. K., & Lee, R. E. (2016). An exploratory decision tree analysis to predict cardiovascular disease risk in African American women. Health Psychology, 35(4), 397.

    Google Scholar 

  47. 47.

    Mittal, K., Khanduja, D., & Tewari, P. C. (2017). An insight into ‘Decision Tree Analysis’”. World Wide Journal of Multidisciplinary Research and Development, 3(12), 111–115.

    Google Scholar 

  48. 48.

    Noormanshah, W. M., Nohuddin, P. N., & Zainol, Z. (2018). Document categorization using decision tree: Preliminary study. International Journal of Engineering & Technology, 7(4.34), 437–440.

    Google Scholar 

  49. 49.

    Patel, B. N., Prajapati, S. G., & Lakhtaria, K. I. (2012). Efficient classification of data using decision tree. Bonfring International Journal of Data Mining, 2(1), 06–12.

    Google Scholar 

  50. 50.

    Ying, Y., Li, J. X., Chen, J. C., Jie, C., Lu, X. F., Chen, S. F., et al. (2011). Effect of elevated total cholesterol level and hypertension on the risk of fatal cardiovascular disease: A cohort study of Chinese steelworkers. Chinese Medical Journal, 124(22), 3702–3706.

    Google Scholar 

  51. 51.

    Rosenson, R. S. (2005). Low high-density lipoprotein cholesterol disorders and cardiovascular risk: Contribution of associated low-density lipoprotein subclass abnormalities. Current Opinion in Cardiology, 20(4), 313–317.

    Google Scholar 

  52. 52.

    Israel, A., & Grossman, E. (2017). Elevated high-density lipoprotein cholesterol is associated with hyponatremia in hypertensive patients. The American Journal of Medicine, 130(11), 1324-e7.

    Google Scholar 

  53. 53.

    Cunha, J. P. HDL vs. LDL cholesterol differences, normal ranges, and meanings. Retrieved February 19, 2019, from www.medicinenet.com.

  54. 54.

    Musich, S., Wang, S. S., Hawkins, K., & Greame, C. (2017). The frequency and health benefits of physical activity for older adults. Population Health Management, 20(3), 199–207. https://doi.org/10.1089/pop.2016.0071.

    Article  Google Scholar 

  55. 55.

    Börjesson, M., Kjeldsen, S., & Dahlöf, B. (2010). Hypertension. Physical activity in the prevention and treatment of disease (2nd ed., pp. 410–425). Professional Associations for Physical Activity: Östersund.

    Google Scholar 

  56. 56.

    Tolonen, H., Wolf, H., Jakovljevic, D., & Kuulasmaa, K. (2002). Smoking: Definitions. Review of surveys for risk factors of major chronic diseases and comparability of the results. European health risk monitoring (EHRM) project. Retrieved September 19, 2019, from https://www.thl.fi/publications/ehrm/product1/title.htm.

  57. 57.

    Ascher-Svanum, H., Zhu, B., Faries, D. E., Furiak, N. M., & Montgomery, W. (2009). Medication adherence levels and differential use of mental-health services in the treatment of schizophrenia. BMC Research Notes, 2(1), 6.

    Google Scholar 

  58. 58.

    World Health Organization. (2019). Body mass index.Retrieved February 19, 2019, from http://www.euro.who.int/en/health-topics/disease-prevention/nutrition/a-healthy-lifestyle/body-mass-index-bmi.

  59. 59.

    Kareem, I. A., & Duaimi, M. G. (2014). Improved accuracy for decision tree algorithm based on unsupervised discretization. International Journal of Computer Science and Mobile Computing, 3(6), 176–183.

    Google Scholar 

Download references

Acknowledgements

We would like to thank the Thailand Research Fund (TRF) and Mae Fah Luang University for their joint support through the Royal Golden Jubilee Ph.D. (RGJ-PHD) Program (Grant No. PHD/0081/2560). Also, we thank Ekkapob Pianpises, M.D. and hospitals in Chiang Rai, Thailand, for assistance with providing insight and expertise, as well as the secondary data used in this study.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Punnarumol Temdee.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chaising, S., Temdee, P. Determining Significant Risk Factors for Preventing Elderly People with Hypertension from Cardiovascular Disease Complication Using Maximum Objective Distance. Wireless Pers Commun 115, 3099–3122 (2020). https://doi.org/10.1007/s11277-020-07195-4

Download citation

Keywords

  • Cardiovascular disease (CVD)
  • Hypertension
  • Significant risk factors
  • Elderly people
  • Objective distance
  • Decision tree