Prevalence and Associated Factors of Hypertension Among Adolescents: A Machine Learning Approach
Predicting Adolescent Hypertension
DOI:
https://doi.org/10.22317/jcms.v12i1.2093Keywords:
Prevalence, Risk Factors, Hypertension, Adolescents, Machine learningAbstract
Objective: This study aims to estimate the prevalence of HTN among high school students in Erbil City, Kurdistan Region, Iraq, and to identify associated factors using a machine learning approach.
Methods: A school-based cross-sectional study was conducted (n = 1619). Eight distinct supervised learning algorithms included Logistic Regression (LR), k-Nearest Neighbors (kNN), Decision Tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Naïve Bayes (NB), and Support Vector Machine (SVM), were implemented to compare the predictive performance. The performance of each model was assessed using the following metrics: Area Under the Receiver Operating Characteristic Curve (AUC-ROC), Area Under the Precision-Recall Curve (AUC-PR), Balanced Accuracy, Precision, Recall, Specificity, and F1 score. The best-performing model was interpreted using SHapley Additive exPlanations.
Results: The prevalence of HTN was 11.3% (95% CI: 9.8% - 13.0%). The highest (AUC-ROC) value (0.8306) was observed for the LR model. However, the XGBoost, RF, and GBM models exhibited slightly lower AUC-ROC values (0.8173, 0.8078, and 0.8041, respectively). Key factors for HTN prediction included higher salt intake, higher BMI, older age, higher sedentary behavior (hr/day), lower physical activity (days/week), positive family history of HTN, female sex, lower vegetable intake, lower sleep duration (hr/night), and lower physical activity (min/day).
Conclusion: This study demonstrated that machine learning algorithms, particularly LR and XGBoost, provide high discriminative power for predicting adolescent hypertension. Future research should focus on validating these models across diverse geographic cohorts to ensure generalizability.
References
Laatikainen T, Nissinen A, Kastarinen M, Jula A, Tuomilehto J. Blood Pressure, Sodium Intake, and Hypertension Control: Lessons From the North Karelia Project. Glob Heart. 2016 Jun 1;11(2):191.
Mills KT, Bundy JD, Kelly TN, Reed JE, Kearney PM, Reynolds K, et al. Global Disparities of Hypertension Prevalence and Control. Circulation. 2016 Aug 9;134(6):441–50.
Lurbe E, Torro I, Alvarez V, Nawrot T, Paya R, Redon J, et al. Prevalence, Persistence, and Clinical Significance of Masked Hypertension in Youth. Hypertension. 2005 Apr;45(4):493–8.
Azegami T, Uchida K, Tokumura M, Mori M. Blood Pressure Tracking From Childhood to Adulthood. Front Pediatr. 2021 Nov 15;9.
National High Blood Pressure Education Program Working Group on High Blood Pressure in Children and Adolescents. The fourth report on the diagnosis, evaluation, and treatment of high blood pressure in children and adolescents. Pediatrics. 2004 Aug;114(2 Suppl 4th Report):555–76.
Juhász M, Katona É, Settakis G, Paragh G, Molnár C, Fülesdi B, et al. Gender-Related Differences in Adolescent Hypertension and in Target Organ Effects. J Womens Health. 2010 Apr;19(4):759–65.
Araujo-Moura K, Souza L, de Oliveira TA, Rocha MS, De Moraes ACF, Chiavegatto Filho A. Prediction of Hypertension in the Pediatric Population Using Machine Learning and Transfer Learning: A Multicentric Analysis of the SAYCARE Study. Int J Public Health. 2025 Mar 11;70.
Deo RC. Machine Learning in Medicine. Circulation. 2015 Nov 17;132(20):1920–30.
Abdul Majeed AA, Haleem AA. Prevalence of hypertension and its associated risk factors among secondary school students in Duhok City. Healthc Low Resour Settings [Internet]. 2024 Mar 21; Available from: https://www.pagepressjournals.org/hls/article/view/12073
Saka M, Shabu S, Shabila N. Prevalence of hypertension and associated risk factors in older adults in Kurdistan, Iraq. Eastern Mediterranean Health Journal. 2020;26(3):265–72.
Song P, Zhang Y, Yu J, Zha M, Zhu Y, Rahimi K, et al. Global Prevalence of Hypertension in Children. JAMA Pediatr. 2019 Dec 1;173(12):1154.
WHO. Noncommunicable Disease Surveillance, Monitoring and Reporting [Internet]. [cited 2024 Sep 3]. Available from: https://www.who.int/teams/noncommunicable-diseases/surveillance/systems-tools/steps
Wattanapisit A, Ng CJ, Angkurawaranon C, Wattanapisit S, Chaovalit S, Stoutenberg M. Summary and application of the WHO 2020 physical activity guidelines for patients with essential hypertension in primary care. Vol. 8, Heliyon. Elsevier Ltd; 2022.
Peltzer K, Pengpid S. Fruits and Vegetables Consumption and Associated Factors among In-School Adolescents in Five Southeast Asian Countries. Int J Environ Res Public Health. 2012 Oct 11;9(10):3575–87.
Kuciene R, Dulskiene V. Associations of short sleep duration with prehypertension and hypertension among Lithuanian children and adolescents: a cross-sectional study. BMC Public Health. 2014 Dec 15;14(1):255.
WHO. BMI-for-age (5-19 years) [Internet]. [cited 2025 Feb 10]. Available from: https://www.who.int/tools/growth-reference-data-for-5to19-years/indicators/bmi-for-age
Flynn JT, Kaelber DC, Baker-Smith CM. Clinical Practice Guideline for Screening and Management of High Blood Pressure in Children and Adolescents [Internet]. Vol. 140, Pediatrics. 2017. Available from: http://publications.aap.org/pediatrics/article-pdf/140/3/e20171904/1104403/peds_20171904.pdf
Granitto PM, Furlanello C, Biasioli F, Gasperi F. Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products. Chemometrics and Intelligent Laboratory Systems. 2006 Sep;83(2):83–90.
Martínez-García M, Gutiérrez-Esparza GO, Márquez MF, Amezcua-Guerra LM, Hernández-Lemus E. Machine learning analysis of emerging risk factors for early-onset hypertension in the Tlalpan 2020 cohort. Front Cardiovasc Med. 2025 Jan 17;11.
Islam MdM, Alam MdJ, Maniruzzaman M, Ahmed NAMF, Ali MS, Rahman MdJ, et al. Predicting the risk of hypertension using machine learning algorithms: A cross sectional study in Ethiopia. PLoS One. 2023 Aug 24;18(8):e0289613.
Seo JW, Lee S, Yim MH. Machine Learning Approach for Predicting Hypertension Based on Body Composition in South Korean Adults. Bioengineering. 2024 Sep 14;11(9):921.
Pandey S, Pandey A, Neupane A, Subedi D, Guragain A. Development of a Hypertension Risk Prediction Model using Nationally Representative Survey Data: A Machine Learning Approach and Web Application Deployment. 2025.
Petracco AM, Mattiello R, Bortolotto CC, Ferreira RW, Matijasevich A, de Barros FCLF, et al. Prevalence of and Factors Associated With High Blood Pressure at 15 Years of Age: A Birth Cohort Study. J Am Heart Assoc. 2023 Dec 5;12(23).
Maric GD, Dusanovic MG, Kostic A V., Pekmezovic TD, Kisic-Tepavcevic DB. Prevalence of hypertension in a sample of schoolchildren in the Belgrade district. Blood Press Monit. 2016 Jun;21(3):155–9.
Saeed NY, Al-Ani MM, Khudhur WY. Prevalence of hypertension among intermediate school children in Ramadi city, west of Iraq. Journal of Emergency Medicine, Trauma and Acute Care. 2022 Dec 1;2022(6).
Badi MAH, Garcia-Triana BE, Suarez-Martinez R. Overweight/obesity and hypertension in schoolchildren aged 6–16 years, Aden governorate, Yemen, 2009. Eastern Mediterranean Health Journal. 2012;18(7).
Soua S, Ghammam R, Maatoug J, Zammit N, Ben Fredj S, Martinez F, et al. The prevalence of high blood pressure and its determinants among Tunisian adolescents. J Hum Hypertens. 2022 Apr 8;38(4):371–9.
Bandy A, Qarmush MM, Alrwilly AR, Albadi AA, Alshammari AT, Aldawasri MM. Hypertension and its risk factors among male adolescents in intermediate and secondary schools in Sakaka City, Aljouf Region of Saudi Arabia. Niger J Clin Pract. 2019;22(8).
Abdulle A, Al-Junaibi A, Nagelkerke N. High Blood Pressure and Its Association with Body Weight among Children and Adolescents in the United Arab Emirates. PLoS One. 2014 Jan 20;9(1):e85129.
Murad NS, Miro SS, Ismael VAH, Abdulah DM. Modifiable risk factors for cardiovascular disease in Iraqi Kurdistan population: a large epidemiological study. Healthc Low Resour Settings. 2023 Dec 19;
Qadir MS, M. Weli S. Prevalence of Cardiovascular Disease Risk Factors Among Secondary School Pupils in Sulaimani City Kurdistan-Iraq. A Cross-Sectional Study. J Fac Med Baghdad. 2023 Jul 1;65(2):129–34.
Sudikno S, Mubasyiroh R, Rachmalina R, Arfines PP, Puspita T. Prevalence and associated factors for prehypertension and hypertension among Indonesian adolescents: a cross-sectional community survey. BMJ Open. 2023 Mar 23;13(3).
Madsen H, Sen A, Aune D. Fruit and vegetable consumption and the risk of hypertension: a systematic review and meta-analysis of prospective studies. Eur J Nutr. 2023 Aug 27;62(5):1941–55.
Leyvraz M, Chatelan A, da Costa BR, Taffé P, Paradis G, Bovet P, et al. Sodium intake and blood pressure in children and adolescents: a systematic review and meta-analysis of experimental and observational studies. Int J Epidemiol. 2018 Dec 1;47(6):1796–810.
Liu X, Zhang D, Liu Y, Sun X, Han C, Wang B, et al. Dose–Response Association Between Physical Activity and Incident Hypertension. Hypertension. 2017 May;69(5):813–20.
Lee PH, Wong FKY. The Association Between Time Spent in Sedentary Behaviors and Blood Pressure: A Systematic Review and Meta-Analysis. Sports Medicine. 2015 Jun 8;45(6):867–80.
Hall JE, da Silva AA, do Carmo JM, Dubinion J, Hamza S, Munusamy S, et al. Obesity-induced Hypertension: Role of Sympathetic Nervous System, Leptin, and Melanocortins. Journal of Biological Chemistry. 2010 Jun;285(23):17271–6.
Kurniawan R, Utomo B, Siregar KN, Ramli K, Besral B, Suhatril RJ, et al. Hypertension prediction using machine learning algorithm among Indonesian adults. IAES International Journal of Artificial Intelligence (IJ-AI). 2023 Jun 1;12(2):776.
Hwang SH, Lee H, Lee JH, Lee M, Koyanagi A, Smith L, et al. Machine Learning–Based Prediction for Incident Hypertension Based on Regular Health Checkup Data: Derivation and Validation in 2 Independent Nationwide Cohorts in South Korea and Japan. J Med Internet Res. 2024 Nov 5;26:e52794.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Journal of Contemporary Medical Sciences
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
