Machine Learning Analysis of Blood Glucose Regulation in Korean Male Workers with Type 2 Diabetes
Abstract
No Abstract. not abstract.
1. Byeon H (2022). Exploring the risk factors of impaired fasting glucose in middle-aged population living in South Korean communities by using categorical boost-ing machine. Front Endocrinol (Lausanne), 13: 1013162.
2. Byeon H (2023). Developing a nomogram for predicting depression in diabetic pa-tients after COVID-19 using machine learning. Front Public Health, 11: 1150818.
3. Shamshirgaran S, Mamaghanian A, Ali-asgarzadeh A, Aiminisani N, Iranparvar-Alamdari M, Ataie J (2017). Age differ-ences in diabetes-related complications and glycemic control. BMC Endocr Dis-ord, 17(1): 25.
4. Ezhilarasi K, Rajendran R, Vanisree AJ (2018). Association of BMI with glyce-mic control in type 2 diabetes patients: A cross-sectional study in Chennai, South India. Biomed Pharmacol J, 11(2): 913-919.
5. Papelbaum M, Moreira R, Coutinho W, Kupfer R, Zagury L, Freitas S, Appo-linario JC (2011). Depression, glycemic control and type 2 diabetes. Diabetol Metab Syndr, 3(26).
6. Li Y, Yu L, Liu Z, et al (2022). Dietary pat-tern associated with the risk of poor glycemic control in Chinese diabetic adults: Results from the China Nutrition and Health Surveillance 2015–2017 Sur-vey. Nutrients, 15(1): 56.
2. Byeon H (2023). Developing a nomogram for predicting depression in diabetic pa-tients after COVID-19 using machine learning. Front Public Health, 11: 1150818.
3. Shamshirgaran S, Mamaghanian A, Ali-asgarzadeh A, Aiminisani N, Iranparvar-Alamdari M, Ataie J (2017). Age differ-ences in diabetes-related complications and glycemic control. BMC Endocr Dis-ord, 17(1): 25.
4. Ezhilarasi K, Rajendran R, Vanisree AJ (2018). Association of BMI with glyce-mic control in type 2 diabetes patients: A cross-sectional study in Chennai, South India. Biomed Pharmacol J, 11(2): 913-919.
5. Papelbaum M, Moreira R, Coutinho W, Kupfer R, Zagury L, Freitas S, Appo-linario JC (2011). Depression, glycemic control and type 2 diabetes. Diabetol Metab Syndr, 3(26).
6. Li Y, Yu L, Liu Z, et al (2022). Dietary pat-tern associated with the risk of poor glycemic control in Chinese diabetic adults: Results from the China Nutrition and Health Surveillance 2015–2017 Sur-vey. Nutrients, 15(1): 56.
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| Issue | Vol 54 No 4 (2025) | |
| Section | Letter to the Editor | |
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How to Cite
1.
Byeon H. Machine Learning Analysis of Blood Glucose Regulation in Korean Male Workers with Type 2 Diabetes. Iran J Public Health. 2025;54(4):888-890.
