ORCID ID
Wan Muhamad Amir W Ahmad : 0000-0003-2366-3918
Mohamad Nasarudin Bin Adnan : 0009-0008-4759-4869
Norhayati Yusop : 0000-0002-2867-1290
Hazik Bin Shahzad : 0000-0002-6524-7261
Farah Muna Mohamad Ghazali : 0000-0001-6772-6779
Nor Azlida Aleng : 0000-0003-1111-3388
Nor Farid Mohd Noor : 0000-0001-6432-781X
Abstract
Background: Hypertension is characterized by abnormally high arterial blood pressure and is a public health problem with a high prevalence of 20%–30% worldwide. This research combined multiple logistic regression (MLR) and multilayer feedforward neural networks to construct and validate a model for evaluating the factors linked with hypertension in patients with dyslipidemia.
Methods: A total of 1000 data entries from Hospital Universiti Sains Malaysia and advanced computational statistical modeling methodologies were used to evaluate seven traits associated with hypertension. R-Studio software was utilized. Each sample's statistics were calculated using a hybrid model that included bootstrapping.
Results: Variable validation was performed by using the well-established bootstrap-integrated MLR technique. All variables affected the hazard ratio as follows: total cholesterol (β1: −0.00664; p < 0.25), diabetes status (β2: 0.62332; p < 0.25), diastolic reading (β3: 0.08160; p < 0.25), height measurement (β4: −0.05411; p < 0.25), coronary heart disease incidence (β5: 1.42544; p < 0.25), triglyceride reading (β6: 0.00616; p < 0.25), and waist reading (β7: −0.00158; p < 0.25).
Conclusions: A hybrid approach was developed and extensively tested. The hybrid technique is superior to other standalone techniques and allows an improved understanding of the influence of variables on outcomes.
References
- Kiau BB, Kau J, Nainu BM, Omar MA, Saleh M, Keong YW, et al. Prevalence, awareness, treatment and control of Hypertension among the elderly: the 2006 National Health and Morbidity Survey III in Malaysia. Med J Malay. 2013;68:332–7.
- Angell SY, De Cock KM, Frieden TR. A public health approach to global management of hypertension. Lancet. 2015;385:825–7.
- Salem H, Hasan DM, Eameash A, El-Mageed HA, Hasan S, Ali R. Worldwide prevalence of hypertension: A pooled meta-analysis of 1670 studies in 71 countries with 29.5 million participants. J Am Coll Cardiol. 2018;71:A1819-A.
- Dai H, Bragazzi NL, Younis A, Zhong W, Liu X, Wu J, et al. Worldwide prevalence, mortality, and disability-adjusted life years trends for hypertensive heart disease from 1990 to 2017. Hypertension. 2021;77:1223–33.
- Collaboration APCS. Blood pressure and cardiovascular disease in the Asia Pacific region. J Hypertension. 2003;21:707–16.
- O'brien E. The Lancet Commission on hypertension: addressing the global burden of raised blood pressure on current and future generations. J Clin Hypertension. 2017;19:564.
- Falaschetti E, Mindell J, Knott C, Poulter N. Hypertension management in England: a serial cross-sectional study from 1994 to 2011. Lancet. 2014;383:1912–9.
- Indrapal M, Nagalla B, Varanasi B, Rachakulla H, Avula L. Socio-demographic factors, overweight/obesity and nutrients associated with hypertension among rural adults (≥ 18 years): Findings from National Nutrition Monitoring Bureau survey. Indian Heart J. 2022;74:382–90.
- Nowbar AN, Gitto M, Howard JP, Francis DP, Al-Lamee R. Mortality from ischemic heart disease: Analysis of data from the World Health Organization and coronary artery disease risk factors from NCD Risk Factor Collaboration. Circ Cardiovasc Qual. 2019;12:e005375.
- Mills KT, Bundy JD, Kelly TN, Reed JE, Kearney PM, Reynolds K, et al. Global hypertension prevalence and control disparities: a systematic analysis of population-based studies from 90 countries. Circulation. 2016;134:441–50.
- Zhou B, Bentham J, Di Cesare M, Bixby H, Danaei G, Cowan MJ, et al. Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19·1 million participants. Lancet. 2017;389:37–55.
- Kearney PM, Whelton M, Reynolds K, Muntner P, Whelton PK, He J. Global burden of hypertension: analysis of worldwide data. Lancet. 2005;365:217–23.
- Soo MJ, Chow ZY, Ching SM, Tan CH, Lee KW, Devaraj NK, et al. Prevalence, awareness and control of hypertension in Malaysia from 1980-2018: A systematic review and meta-analysis. World J Meta Anal. 2020;8:320–44.
- Huang Y, Cai X, Liu C, Zhu D, Hua J, Hu Y, et al. Prehypertension and the risk of coronary heart disease in Asian and western populations: a meta‐analysis. J Am Heart Assoc. 2015;4:e001519.
- Andersson C, Johnson AD, Benjamin EJ, Levy D, Vasan RS. 70-year legacy of the framingham heart study. Nat Rev Cardiol. 2019;16:687–98.
- Agarwala A, Mehta A, Yang E, Parapid B. Older adults and hypertension: Beyond the 2017 guideline for prevention, detection, evaluation, and management of high blood pressure in adults. Washington, DC: American College of Cardiology; 2020.
- Xiao Y, Liu Y, Zheng S, Yang Y, Fan S, Yang C, et al. Relationship between hypertension and body mass index, waist circumference and waist-hip ratio in middle-aged and elderly residents. Zhonghua Liu Xing Bing Xue Za Zhi. 2016;37:1223–7.
- Carey RM, Muntner P, Bosworth HB, Whelton PK. Prevention and control of hypertension: JACC health promotion series. J Am Coll Cardiol. 2018;72:1278–93.
- Poorolajal J, Farbakhsh F, Mahjub H, Bidarafsh A, Babaee E. How much excess body weight, blood sugar, or age can double the risk of hypertension? Pub Health. 2016;133:14–8.
- Jain HR, Shetty V, Singh G, Shetty S. A study of lipid profile in diabetes mellitus. Int. J. Sci. Stud. 2016;4:55–60.
- Lonardo A, Nascimbeni F, Mantovani A, Targher G. Hypertension, diabetes, atherosclerosis and NASH: cause or consequence? J Hepat. 2018;68:335–52.
- Shahid N, Rappon T, Berta W. Applications of artificial neural networks in health care organizational decision-making: A scoping review. PLoS One. 2019;14:e0212356.
- Mills KT, Stefanescu A, He J. The global epidemiology of hypertension. Nat Rev Nephrol. 2020;16:223–37.
- Mickey RM, Greenland S. The impact of confounder selection criteria on effect estimation. Am J Epidemiol. 1989;129:125–37.
- Hosmer Jr DW, Lemeshow S, Sturdivant RX. Applied logistic regression. USA: John Wiley & Sons, Inc.; 2013.
- Ahmad WMAW, Adnan MNB, Ibrahim MSM, Samsudin NA, Noor NFM, Aleng NA, et al. Developing a hybrid linear model with a multilayer feed-forward neural network for HbA1c modeling among diabetes patients. Asian J Fund Appl Sci. 2023;4:41–9.
- Ahmad WMAW, Shahzad HB, Adnan MN, Ghazali FMM, Mohamad N, Noor NFM, et al. A variable selection in ordered logistic regression model using decision tree analysis for the classification: a case study of hypertension modeling. Eur J Mol Clin Med. 2023;10:3367–3378.
- Adeleke K, Adepoju A. Ordinal logistic regression model: An application to pregnancy outcomes. J Math Stat. 2010;6:279–285.
- Efron B. The jackknife, the bootstrap and other resampling plans. USA: SIAM; 1982.
- Nguyen QH, Ly H-B, Ho LS, Al-Ansari N, Le HV, Tran VQ, et al. Influence of data splitting on performance of machine learning models in prediction of shear strength of soil. Math Probl Eng. 2021;2021:1–15.
- Chang C-D, Wang C-C, Jiang BC. Using data mining techniques for multi-diseases prediction modeling of hypertension and hyperlipidemia by common risk factors. Expert Syst Appl. 2011;38:5507–13.
- Akdag B, Fenkci S, Degirmencioglu S, Rota S, Sermez Y, Camdeviren H. Determination of risk factors for hypertension through the classification tree method. Adv Ther. 2006;23:885–92.
- AlKaabi LA, Ahmed LS, Al Attiyah MF, Abdel-Rahman ME. Predicting hypertension using machine learning: Findings from Qatar Biobank Study. PLoS One. 2020;15:e0240370.
- Dimitriadis G, Mitrou P, Lambadiari V, Maratou E, Raptis SA. Insulin effects in muscle and adipose tissue. Diabetes Res. Clin. Pr. 2011;93:S52–9.
Recommended Citation
Ahmad WM, Adnan MN, Yusop N, Shahzad HB, Ghazali FM, Aleng NA, et al. Prediction of Factors for Patients with Hypertension and Dyslipidemia Using Multilayer Feedforward Neural Networks and Ordered Logistic Regression Analysis: A Robust Hybrid Methodology. Makara J Health Res. 2023;27.
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