Determinants of blood pressure control in hypertensive individuals using histogram-based gradient boosting: findings from 1114 male workers in South Korea

Hypertension is a significant public health concern, particularly among workers, due to its association with increased risk of cardiovascular and cerebrovascular diseases. This study aimed to identify key factors influencing blood pressure control in hypertensive male workers aged 40 and above using the Histogram-based Gradient Boosting (HGB) algorithm. Data were drawn from the 2017–2020 Korean National Health and Nutrition Examination Survey (KNHANES), including 1114 male participants who reported being diagnosed with hypertension by a physician. The HGB model was compared with five other machine learning models: Random Forest, XGBoost, LightGBM, CatBoost and AdaBoost. The HGB model demonstrated superior performance with an accuracy of 82.3%, precision of 80.5%, recall of 78.9% and F1-score of 79.7%. Feature importance analysis revealed that age, Body Mass Index (BMI) and physical activity were the most significant factors influencing blood pressure control. Other notable factors included sodium intake, stress levels and medication adherence. The study’s findings underscore the importance of targeted interventions focusing on these key factors to improve hypertension management strategies. By employing advanced machine learning techniques, this research provides valuable insights into the determinants of blood pressure control, offering a foundation for developing effective strategies to reduce hypertension-related complications and mortality among Korean male workers.

Hypertension; Blood pressure control; Machine learning; Gradient boosting; Male worker

[1] Arafa A, Lee HH, Eshak E, Shirai K, Liu K, Li J, et al. Modifiable risk factors for cardiovascular disease in Korea and Japan. The Korean Journal of Cardiology. 2021; 51: 643–655.

[2] Park D, Shin MJ, Despres J, Eckel R, Tuomilehto J, Lim S. 20-year trends in metabolic syndrome among Korean adults from 2001 to 2020. Journal of the American College of Cardiology. 2023; 3: 491–502.

[3] Lee M, Lee H, Park J, Kim HJ, Kwon R, Lee SW, et al. Trends in hypertension prevalence, awareness, treatment, and control in South Korea, 1998–2021: a nationally representative serial study. Scientific Reports. 2023; 13: 21724.

[4] Truong PV, Apriliyasari RW, Lin MY, Chiu HY, Tsai P. Effects of self-management programs on blood pressure, self-efficacy, dietary adherence and body mass index in older adults with hypertension: meta-analysis of randomized controlled trials. International Journal of Nursing. 2021; 27: e12920.

[5] Jeoung B, Kim J. A cross-sectional study of physical fitness and risk of hypertension in Korean adults aged 20–59. Healthcare. 2023; 11: 2057.

[6] Sarfika R, Sulistiawati, Afriyanti E, Saifudin I. Self-care behavior among adult patients with hypertension in Padang, West Sumatra, Indonesia: a cross-sectional study. Belenus Nursing Journal. 2023; 9: 595–602.

[7] Biddinger KJ, Emdin C, Haas ME, Wang M, Hindy G, Ellinor P, et al. Association of habitual alcohol intake with risk of cardiovascular disease. JAMA Network Open. 2022; 5: e223849.

[8] Longkumer I, Yadav S, Rajkumari S, Saraswathy K. Trends in hypertension prevalence, awareness, treatment, and control: an 8-year follow-up study from rural North India. Scientific Reports. 2023; 13: 9910.

[9] Felek D, Çiğci A. Approach to hypertension in geriatric population. Journal of Cardiology & Current Views. 2023; 1: 45–48.

[10] Fatimah S, Utami F. The influence of elderly exercise on blood pressure changes in elderly with hypertension in the Areas of Kp. Dukuh II and Dukuh III Kramat Jati East Jakarta. Jurnal Ilmiah Kesehatan. 2021; 13: 156–164.

[11] Fata UH, Rahmawati A, Wibisono W, Aktyani Putri RRD. Compliance with a low salt diet in elderly hypertension at the elderly posyandu, Gedog village, Sananwetan district, Blitar city. Journal of Nursing and Kesehatan. 2023; 10: 177–181.

[12] Zahidah NN. Literature review: low salt diet in patient with hypertension. Indonesian Medical & Health Science Journal. 2021; 5: 224–231.

[13] Solomon M, Negussie Y, Bekele NT, Getahun MS, Gurara A. Uncontrolled blood pressure and associated factors in adult hypertensive patients undergoing follow-up at public health facility ambulatory clinics in Bishoftu town, Ethiopia: a multi-center study. BMC Cardiovascular Disorders. 2023; 23: 258.

[14] Riyadina W, Rahajeng E, Turana Y, Kodim N. Hypertension in postmenopausal Indonesian women: fluctuating body mass index as a potential predictor of decreasing blood pressure. African Health Sciences. 2022; 22: 264–272.

[15] Nikbakht HR, Najafi F, Shakiba E, Darbandi M, Navabi J, Pasdar Y. Triglyceride glucose-body mass index and hypertension risk in Iranian adults: a population-based study. BMC Endocrine Disorders. 2023; 23: 156.

[16] Mirzaei M, Mirzaei M, Bagheri B, Dehghani A. Awareness, treatment, and control of hypertension and related factors in adult Iranian population. BMC Public Health. 2020; 20: 667.

[17] Kashifi MT, Ahmad I. Efficient histogram-based gradient boosting approach for accident severity prediction with multisource data. Transportation Research Record. 2022; 2676: 236–258.

[18] Marvin G, Grbčić L, Družeta S, Kranjčević L. Water distribution network leak localization with histogram-based gradient boosting. Journal of Hydroinformatics. 2023; 25: 663–684.

[19] Ahn JM, Kim J, Kim K. Ensemble machine learning of gradient boosting (XGBoost, LightGBM, CatBoost) and attention-based CNN-LSTM for harmful algal blooms forecasting. Toxins. 2023; 15: 608.

[20] Chowdhury M, Leung A, Walker R, Sikdar K, O’Beirne M, Quan H, et al. A comparison of machine learning algorithms and traditional regression-based statistical modeling for predicting hypertension incidence in a Canadian population. Scientific Reports. 2023; 13: 13.

[21] Xu K, Han Z, Xu H, Bin L. Rapid prediction model for urban floods based on a light gradient boosting machine approach and hydrological-hydraulic model. International Journal of Disaster Risk Science. 2023; 14: 79–97.