Acknowledgement
이 성과는 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(No. 2022R1F1A1067604).
References
- Doyle AE. Hypertension and vascular disease. Am J Hypertens. 1991;4(2):103-106. https://doi.org/10.1093/ajh/4.2.103S
- Samadian F, Dalili N, Jamalian A. Lifestyle Modifications to Prevent and Control Hypertension. Iran J Kidney Dis. 2016;10(5):237-63.
- Wu L, Yang S, He Y, Liu M, Wang Y, Wang J, et al. Association between passive smoking and hypertension in Chinese non-smoking elderly women. Hypertens Res. 2017;40(4):399-404. https://doi.org/10.1038/hr.2016.162
- Wang Y, Yao Y, Chen Y, Zhou J, Wu Y, Fu C, et al. Association between Drinking Patterns and Incident Hypertension in Southwest China. Int J Res Public Health. 2022;19(7):3801
- Kulkarni S, O'Farrell I, Erasi M, Kochar MS. Stress and hypertension. WMJ. 1998;97(11):34-8.
- Byeon HW, Cho SH. The Predictive Modeling of Middle-aged Hypertension using Integrated Method of Decision Tree and Neural Network. AJMAHS. 2015;5(2):9-18. https://doi.org/10.14257/AJMAHS.2015.04.25
- ou Y, Teng W, Wang J, Ma G, Ma A, Wang J, et al. Hypertension and physical activity in middle-aged and older adults in China. Sci Rep. 2018;8(1):16098.
- Kim HS, Jung SH, Park SK. Decision-Tree Analysis to Predict Blood Pressure Control Status among Hypertension Patients Taking Antihypertensive Medications. J Korean Biol Nurs Sci. 2019;21(1):85-97. https://doi.org/10.7586/jkbns.2019.21.1.85
- Zhao H, Zhang X, Xu Y, Gao L, Ma Z, Sun Y, et al. Predicting the Risk of Hypertension Based on Several Easy-to-Collect Risk Factors: A Machine Learning Method. Front Public Health. 2021;9:619429.
- Anh DT, Takakura H, Asai M, Ueda N, Shojaku H. Application of machine learning in the diagnosis of vestibular disease. Sci Rep. 2022;12(1):20805.
- Dritsas E, Trigka M. Machine Learning Methods for Hypercholesterolemia Long-Term Risk Prediction. Sensors (Basel). 2022;22(14):5365.
- Heo J, Yoon JG, Park H, Kim YD, Nam HS, Heo JH. Machine Learning-Based Model for Prediction of Outcomes in Acute Stroke. Stroke. 2019;50(5):1263-5. https://doi.org/10.1161/STROKEAHA.118.024293
- Lee YW, Choi JW, Shin EH. Machine learning model for predicting malaria using clinical information. Comput Biol Med. 2021;129:104151.
- Kwon TW, Koo YH. Comparative Analysis of Prediction Taekwondo Trainee's Defection using Decision Tree and Logistic Regression. KSSS. 2008;17(2):71-83.
- Park MH, Choi SR, Shin AM, Koo CH. Analysis of the Characteristics of the Older Adults with Depression Using Data Mining Decision Tree Analysis. J Korean Acad Nurs. 2013;43(1):1-10. https://doi.org/10.4040/jkan.2013.43.1.1
- Choi JH, Seo DS. Decision Trees and Its Applications. JKOS. 1999;4(1):61-83.
- Open Data. Korea Health Panel Annual Data 2019 [Internet]. Sejong and Won ju: Korea Institute for Health and Social Affiairs and National Health Insurance Service; 2020 [cited 2023 Feburary 4]. Available from: https://www.khp.re.kr:444/web/notice/board/view.do?bbsid=53&seq=2832
- Rhee EJ. Current status of obesity treatment in Korea: based on the 2020 Korean Society for the Study of Obesity guidelines for obesity management. J Korean Med Assoc. 2022;65(7):388-92. https://doi.org/10.5124/jkma.2022.65.7.388
- Barsasella D, Bah K, Mishra P, Uddin M, Dhar E, Suryani DL, et al. A Machine Learning Model to Predict Length of Stay and Mortality among Diabetes and Hypertension Inpatients. Medicina (Kaunas). 2022;58(11):1568.
- Oh TS, Kim DK, Won CW, Kim SY, Jeong EJ, Yang JS, et al. A Machine-Learning-Based Risk Factor Analysis for Hypertension: Korea National Health and Nutrition Examination Survey 2016-2019. KJFP. 2022;12(3):173-8. https://doi.org/10.21215/kjfp.2022.12.3.173
- Vasan RS, Beiser A, Seshadri S, Larson MG, Kannel WB, D'Agostino RB, et al. Residual lifetime risk for developing hypertension in middle-aged women and men: The Framingham Heart Study. JAMA. 2002;287(8):1003-10. https://doi.org/10.1001/jama.287.8.1003
- Ghanbari J, Mohammadpoorasl A, Jahangiry L, Farhangi MA, Amirzadeh J, Ponnet K. Subgroups of lifestyle patterns among hypertension patients: a latent-class analysis. BMC Med Res methodol. 2018;18(1):127.
- Kim SI, Woo SJ, Jung YH. Factors Related to Hypertension Patients' Quality of Life: The 7th Korean National Health and Nutrition Examination (1st Year, 2016). Leisure Activity Types and Depressive Symptoms among Middle-Aged People Living Alone JKSSCHE. 2020;21(1):61-74.
- Kwon MS, Noh GY, Jang JH. A study on relationships between health literacy, disease-related knowledge and compliance to medical recommendations in patients with hypertension. J Korean Pubilc Health Nurs. 2013;27(1):190-202.
- Kang EN, Kim HJ, Kim YS. Leisure Activity Types and Depressive Symptoms among Middle-Aged People Living Alone. HSWR. 2017;37(2):184-215.
- Jeon DJ, Kim SH, Park SH, Yoon HJ, Kim SG, Kim JH. The Prevalence and Psychosocial Correlates of Depressive Symptoms in Patients with Hypertension. J Korean Soc Biol Ther Psychiatry. 2019;25(3):213-21.
- Ramezankhani A, Azizi F, Hadaegh F. Associations of marital status with diabetes, hypertension, cardiovascular disease and all-cause mortality: a long term follow-up study. PLoS One. 2019;14(4):e0215593.
- Tuoyire DA, Ayetey H. Gender differences in the association between marital status and hypertension in Ghana. J Biosoc Sci. 2019;51(3):313-34. https://doi.org/10.1017/S0021932018000147
- Lipowicz A, Lopuszanska M. Marital differences in blood pressure and the risk of hypertension among Polish men. Eur J epidemiol. 2005;20(5):421-7. https://doi.org/10.1007/s10654-005-1752-x
- Boutcher YN, Boutcher SH. Exercise intensity and hypertension: what's new? Journal of human hypertension. J Hum Hypertens. 2017;31(3):157-64. https://doi.org/10.1038/jhh.2016.62
- Ruivo JA, Alcantara P. Hypertension and exercise. Rev Port Cardiol. 2012;31(2):151-8. https://doi.org/10.1016/j.repc.2011.12.012
- Day E, Rudd JHF. Alcohol use disorders and the heart. Addiction. 2019;114(9):1670-8. https://doi.org/10.1111/add.14703
- Sleight P. Smoking and hypertension. Clin Exp Hypertens. 1993;15(6):1181-92. https://doi.org/10.3109/10641969309037104
- Choi JH, Park JH, Choi BG. Association between Education Level and Hypertension in Korean Adults Over 30 Years Old: Korea National Health and Nutrition Examination Survey 2019. KJFP. 2022;12(4):247-53. https://doi.org/10.21215/kjfp.2022.12.4.247
- Sohn K. Relationship of smoking to hypertension in a developing country. Glob Heart. 2018;13(4):285-92. https://doi.org/10.1016/j.gheart.2018.01.004
- Lee HS, Kwun IS, Kwon CS. Prevalence of Hypertension and Related Risk Factors of the Older Residents in Andong Rural Area. JKSFSN. 2009; 38(7):852-61. https://doi.org/10.3746/jkfn.2009.38.7.852
- Eom JS, Lee TR, Park SJ, Ahn YJ, Chung YJ. The risk factors of the pre-hypertension and hypertension of rural inhabitants in Chungnam-do. J Nutr Health. 2008;41(8):742-53.
- Lee HJ, Lee HS, Lee YN, Jang YA, Moon JJ, Kim CI. Nutritional environment influences hypertension in the middle-aged Korean adults-Based on 1998 & 2001 National Health and Nutrition Survey. Korean J Community Nutr. 2007;12(3):272-83.
- Ambrose JA, Barua RS. The pathophysiology of cigarette smoking and cardiovascular disease: an update. Journal of the American College of Cardiology. J Am Coll Cardiol. 2004;43(10):1731-7. https://doi.org/10.1016/j.jacc.2003.12.047
- Biddinger KJ, Emdin CA, Haas ME, Wang M, Hindy G, Ellinor PT, et al. Association of Habitual Alcohol Intake With Risk of Cardiovascular Disease. JAMA netw open. 2022;5(3):e223849.
- Di Chiara T, Scaglione A, Corrao S, Argano C, Pinto A, Scaglione R. Education and hypertension: impact on global cardiovascular risk. Acta cardiol. 2017;72(5):507-13. https://doi.org/10.1080/00015385.2017.1297626
- Kim JE. Measuring the Level of Health Literacy and Influence Factors: Targeting the Visitors of a University Hospital's Outpatient Clinic. J Korean Clin Nurs Res. 2011;17(1):40-7.
- Diaz ME. Hypertension and obesity. J Hum Hypertens. 2002;16(1):18-22. https://doi.org/10.1038/sj.jhh.1001335
- Rahman M, Zaman MM, Islam JY, Chowdhury J, Ahsan HN, Rahman R, et al. Prevalence, treatment patterns, and risk factors of hypertension and pre-hypertension among Bangladeshi adults. Journal of human hypertension. J Hum Hypertens. 2018;32(5):334-48. https://doi.org/10.1038/s41371-017-0018-x
- Lee SH, Park YM, Han KD, Yang JH, Lee SW, Lee SS, et al. Obesity-related hypertension: Findings from The Korea National Health and Nutrition Examination Survey 2008-2010. PLoS one. 2020;15(4):e0230616.
- Leenen FH, McInnis NH, Fodor G. Obesity and the prevalence and management of hypertension in Ontario, Canada. Am J Hypertens. 2010;23(9):1000-6. https://doi.org/10.1038/ajh.2010.93
- Choi YH. Is self-rated Health a Sufficient Proxy for True Health? KJGSW. 2018;73(4):7-28. https://doi.org/10.21194/kjgsw.73.4.201812.7
- Chang DM, Park IS, Yang JH. Related Factors of Awareness, Treatment, and Control of Hypertension in Korea : Using the Fourth Korea National Health & Nutrition Examination Survey. J Digit Converge. 2013;3(5):6-7.
- Kim KY. Risk factors for hypertension in elderly people aged 65 and over, and adults under age 65. JKAIS. 2019;20(1):162-9.
- Yi YM, Park YH. Factors Related to Subjective Health Status in Community-Dwelling Older Adults Living Alone on Low Income. The J of Mus and Joint Health. 2022;29(3):205-17.
- Mitchell TM. Machine learning. 1st ed. New York: McGraw Hill; 2007.
- Lee HH, Chung SH, Choi EJ. A case study on machine learning applications and performance improvement in learning algorithm. J Digit Converge. 2016;14(2): 245-258. https://doi.org/10.14400/JDC.2016.14.2.245
- Huang X, Cao T, Chen L, Li J, Tan Z, Xu B, et al. Novel Insights on Establishing Machine Learning-Based Stroke Prediction Models Among Hypertensive Adults. Front Cardiovasc Med. 2022;9:901240.
- Shah W, Aleem M, Iqbal MA, Islam MA, Ahmed U, Srivastava G, et al. A Machine-Learning-Based System for Prediction of Cardiovascular and Chronic Respiratory Diseases. J Healthc Eng. 2021;2021:2621655.
- Park IS, Yong WS, Kim YM, Kang SH, Han JT. A development of a tailored follow up management model using the data mining technique on hypertension. KSS. 2008;21(4):639-47.
- Yoo JE. Random forests, an alternative data mining technique to decision tree. J Educ Evaluation. 2015;28(2):427-448.
- Uddin S, Khan A, Hossain ME, Moni MA. Comparing different supervised machine learning algorithms for disease prediction. BMC. 2019;19(1):281.
- Seo JD. Foreign Exchange Rate Forecasting Using the GARCH extended Random Forest Model. JIEB. 2016;29(5):1607-1628.
- Breiman L. Random forests. Machine learning. 2001;45:5-32. https://doi.org/10.1023/A:1010933404324
- Jeong SW, Lee MJ, Yoo SY. Machine Learning-based Stroke Risk Prediction using Public Big Data. J AdvNaving Technol. 2021;25(1):96-101.
- Hyun JK. Prediction of Diabetic Neuropathy Using Machine Learning Techniques. JKD. 2022;23(4):338-244