A Hybrid Logistic Regression Model with Harris Hawks Optimization (HHO) Algorithm of Hypertension Determinants among Iraqi Adults

Section: Research Article
Issue
Vol. 35 No. 2 (2026): Volume 35, Issue 2
Pages
40-56

Keywords:

: Applied statistics, logistic regression, hypertension, Harris Hawks Optimization

Abstract

Hypertension is a major chronic disease worldwide and in Iraq. Baghdad especially suffers from it due to rapid urbanization, dietary changes, and lifestyle shifts. We lack local data on hypertension factors in Baghdad compared to global studies. This cross-sectional study examined multiple risk factors in 1,050 adults (ages 18-70) in Baghdad during 2023-2024. We used multi-stage stratified sampling. Logistic regression usingthe Harris Hawks Optimization (HHO) algorithm was applied to select the most impactful variables. We studied 11 factors: age, gender, smoking, physical activity, BMI, cholesterol, salt intake, sleep quality, stress, education, and income. HHO was chosen because it handles high-dimensional data efficiently. Age (OR: 2.14) and obesity (BMI ≥30, OR: 3.26) emerged as the strongest predictors of hypertension in Baghdad. The hybrid model achieved 84.2% accuracy andan AUC of 0.87. Standard logistic regression hada lower AUC of 0.79. Age-targeted interventions are needed for hypertension control in Baghdad. Weight management programs are also essential. These results apply to other Middle Eastern cities facing similar epidemiological changes.

References

  1. Ali, A.A., 2016. Prevalence and determinants of microalbuminuria among Type 2 diabetes mellitus patients in Iraq. Saudi Journal of Kidney Diseases and Transplantation, 27(3), pp.524–531.
  2. Al-Nimer, M.S.M., 2017. Subclinical Ventricular Repolarization Abnormality in Iraqi Hypertensive Patients. Indian Heart Journal, 69(4), pp.567–572.
  3. Al-Rubaye, A.A.H., 2024. Risk factors of hypertension in Basra governorate, Iraq. Bioscience Journal, 6(1), pp.102–107.
  4. Al-Zubaidi, A., 2021. Screening for hypertension among adults attending primary healthcare centers in Baghdad. Iraqi Medical Journal, 69(2), pp.75–84.
  5. Azizi, M., 2024. Harris Hawks Optimization Algorithm: Review and Medical Applications. Swarm Intelligence, 18(2), pp.55–78.
  6. Fateh, H.L. et al., 2022. Association between dietary total antioxidant capacity and hypertension. Clinical Nutrition ESPEN, 49, pp.117–124.
  7. Habib, O.S., 1986. The determinants of health services utilization in Southern Iraq. Social Science & Medicine, 23(8), pp.845–849.
  8. Hussein, N.R., Abdi, A., & Naqid, I.A., 2025. Prevalence of diagnosed hypertension and its determinants in Zakho City, Kurdistan, Iraq. Cureus Journal of Medical Science, 17(4), pp.1–10.
  9. Mansour, A.A., 2020. A retrospective analysis of a tertiary care database over 9 years in Iraqi diabetes patients. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(2), pp.123–130.
  10. Mansouri, A. et al., 2023. An analysis of the Global Burden of Hypertension in 2019. BMC Public Health, 23(1), pp.826–839.
  11. Ministry of Health Iraq, 2021. National Survey on Noncommunicable Diseases Risk Factors (Hypertension, Diabetes, Smoking) in Iraq. Baghdad: Ministry of Health and Environment.
  12. Ochmann, S., 2023. Diagnostic testing for hypertension, diabetes, and cardiovascular disease: Global analysis. The Lancet Regional Health, 15(9), pp.112–119.
  13. Pengpid, S., 2021. Overweight and obesity among adults in Iraq and their relation to hypertension. Diabetes & Metabolic Syndrome: Clinical Research Reviews, 15(3), pp.1024–1032.
  14. Qasim, N., 2022. Evaluation of feature selection methods in healthcare data analytics. IEEE Access, 10, pp.75091–75110.
  15. Raza, S., 2024. Enhanced Harris Hawks Algorithm with application to medical datasets. Information Sciences, 613, pp.243–262.
  16. Schutte, A.E., 2023. Addressing global disparities in blood pressure control. International Journal of Hypertension, 2023(1), pp.1–9.
  17. Saka, M.H., Shabu, S.A. & Shabila, N.P., 2025. Prevalence of hypertension and associated risk factors in older adults in Erbil City, Iraq. Eastern Mediterranean Health Journal, 26(3), pp.194–206.
  18. Shaker, M., 2019. Hypertension in Iraq: National prevalence and control challenges. Iraqi Journal of Medical Sciences, 17(4), pp.221–230.
  19. University of Zakho, 2024. Utilizing Multinomial Logistic Regression for determining factors influencing blood pressure. Science Journal of University of Zakho, 12(3), pp.132–140.
  20. Wang, T., 2024. Trends in global hypertension prevalence. Journal of Hypertension, 42(6), pp.1121–1135.
  21. WHO, 2025. Global Status Report on Noncommunicable Diseases. Geneva: World Health Organization, pp.33–54.
  22. WHO, 2023. Hypertension Fact Sheet. World Health Organization, Geneva, pp.15–23.
  23. WHO, 2025. Country Profile: Iraq. Geneva: WHO, pp.4–9.
  24. Yahya, T., 2022. Cardiovascular risk profile of Middle Eastern immigrants: A logistic regression analysis. Journal of Clinical Epidemiology, 145(2), pp.88–96.
  25. Yusuf, S., 2020. Statistical approaches in medical data modeling. Iraqi Journal of Medical Statistics, 5(2), pp.115–129.
  26. Zhuang, Y., 2024. Feature selection in health data: A comparative study. Biomed Research International, 2024(6), pp.1–14.
  27. Zhou, L., 2021. Machine learning models for hypertension risk prediction. Sensors, 21(15), pp.5026–5034.
  28. Mansour, A.A., 2012. Prevalence and control of hypertension in Iraqi diabetic patients. The Open Cardiovascular Medicine Journal, 6(1), pp.68–74.
  29. Ministry of Planning Iraq, 2020. National Demographic and Health Survey of Iraq. Baghdad: Central Statistical Organization, pp.1–210.
  30. World Health Organization, 2025. First WHO report details the impact of hypertension and global strategies to stop it. PAHO/WHO Official Publication, pp.9–18.

Author

Ammar Nasser

Identifiers

DOI: 10.33899/jes.v35i2.53624

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[1]
“A Hybrid Logistic Regression Model with Harris Hawks Optimization (HHO) Algorithm of Hypertension Determinants among Iraqi Adults”, JES, vol. 35, no. 2, pp. 40–56, Apr. 2026, doi: 10.33899/jes.v35i2.53624.
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How to Cite

[1]
“A Hybrid Logistic Regression Model with Harris Hawks Optimization (HHO) Algorithm of Hypertension Determinants among Iraqi Adults”, JES, vol. 35, no. 2, pp. 40–56, Apr. 2026, doi: 10.33899/jes.v35i2.53624.