Analysis of the risk factors and screening model establishment of type 2 diabetes mellitus based on the particle swarm optimization BP neural network_West China Medical Journal

Authors：

WANG Shihong , CHEN Yongze , MAI Zhenhua , TAN Xiwei , YANG Zihua , WANG Shuang , ZHAO Le , MA Yong , HUANG Cuiyi ,  KONG Danli ,  DING Yuanlin ,  YU Haibing

1. School of Public Health, Guangdong Medical University, Dongguan, Guangdong 523808, P. R. China;

Corresponding?author：

KONG Danli, Email: kdlgdmu@gdmu.edu.cn; DING Yuanlin, Email: gdmcsbd@163.com; YU Haibing, Email: hby616688@163.com

Keywords：

Type 2 diabetes mellitus; particle swarm optimization; bp neural network; mean impact value; risk factors

DOI：

10.7507/1002-0179.202311033

Video：

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Objective To analyze the risk factors of type 2 diabetes mellitus and establish BP neural network model for screening of type 2 diabetes mellitus based on particle swarm optimization (PSO) algorithm. Methods Inpatients with type 2 diabetes mellitus in the Department of Endocrinology of the Affiliated Hospital of Guangdong Medical University and the Second Affiliated Hospital of Guangdong Medical University between July 2021 and August 2022 were selected as the case group and healthy people in the Health Management Center of the Affiliated Hospital of Guangdong Medical University as the control group. Basic information and physical and laboratory examination indicators were collected for comparative analysis. PSO-BP neural network model, BP neural network model and logistic regression models were established using MATLAB R2021b software and the optimal screening model of type 2 diabetes mellitus was selected. Based on the optimal model, the mean impact value algorithm was used to screen the risk factors of type 2 diabetes mellitus. Results A total of 1 053 patients were included in the case group and 914 healthy peoples in the control group. Except for type of salt, family history of comorbidities, body mass index, total cholesterol, low density lipoprotein cholesterol and staple food intake (P>0.05), the other indexes showed significant differences between the two groups. The performance of the PSO-BP neural network model outperformed the BP neural network model and the logistic regression model. Based on PSO-BP neural network model, the mean impact value algorithm showed that the risk factors for type 2 diabetes mellitus were fasting blood glucose , heart rate, age , waist-arm ratio and marital status , and the protective factors for type 2 diabetes mellitus were high density lipoprotein cholestero, vegetable intake, residence, education level, fruit intake and meat intake. Conclusions There are many influencing factors of type 2 diabetes mellitus. Focus should be placed on high-risk groups and regular disease screening should be carried out to reduce the risk of type 2 diabetes. The screening model of PSO-BP neural network performs the best, and it can be extended to the early screening and diagnosis of other diseases in the future.

Citation： WANG Shihong, CHEN Yongze, MAI Zhenhua, TAN Xiwei, YANG Zihua, WANG Shuang, ZHAO Le, MA Yong, HUANG Cuiyi, KONG Danli, DING Yuanlin, YU Haibing. Analysis of the risk factors and screening model establishment of type 2 diabetes mellitus based on the particle swarm optimization BP neural network. West China Medical Journal, 2024, 39(2): 251-258. doi: 10.7507/1002-0179.202311033 Copy

1.	付俊玲, 穆志靜, 孫麗娜, 等. 住院 2 型糖尿病患者視網膜病變的危險因素及膽紅素水平分析. 中華糖尿病雜志, 2022, 14(9): 947-953.
2.	姬超琴, 王文絹. 成本效果分析在 2 型糖尿病心血管疾病危險因素控制中的應用. 中國慢性病預防與控制, 2022, 30(10): 797-801.
3.	Deng Y, Xiao H, Xu J, et al. Prediction model of PSO-BP neural network on coliform amount in special food. Saudi J Biol Sci, 2019, 26(6): 1154-1160.
4.	Liu X, Liu Z, Liang Z, et al. PSO-BP neural network-based strain prediction of wind turbine blades. Materials (Basel), 2019, 12(12): 1889.
5.	Mayfield J. Diagnosis and classification of diabetes mellitus: new criteria. Am Fam Physician, 1998, 58(6): 1355-1362, 1369-1370.
6.	Han IJ, Yuan TF, Lee JY, et al. Learned Prediction of Compressive Strength of GGBFS Concrete Using Hybrid Artificial Neural Network Models. Materials (Basel), 2019, 12(22): 3708.
7.	李大虎, 李秋科, 王文才, 等. 基于 MIV 特征選擇與 PSO-BP 神經網絡的煤炭發熱量預測. 煤炭工程, 2020, 52(11): 154-160.
8.	侯哲, 仝衛國. 基于 PSO-BP 神經網絡的脫硫制漿系統能耗建模. 自動化與儀器儀表, 2022(9): 26-29.
9.	路思恒, 尹紅. 基于 BP 神經網絡的居民消費水平的預測模型——以安徽省為例. 現代電子技術, 2022, 45(21): 83-87.
10.	郭中小, 宋一凡, 廖梓龍, 等. 基于 MIV 的遺傳神經網絡徑流預報模型. 人民黃河, 2014, 36(10): 33-35.
11.	Wu Y, Ding Y, Tanaka Y, et al. Risk factors contributing to type 2 diabetes and recent advances in the treatment and prevention. Int J Med Sci, 2014, 11(11): 1185-1200.
12.	盧潮. 中山市住院 2 型糖尿病患者慢性并發癥發病情況調查及危險因素分析. 糖尿病新世界, 2022, 25(6): 38-41.
13.	Yilmaz M, Karaaslan M, Tonyali S, et al. Triglyceride-glucose index (TyG) is associated with erectile dysfunction: a cross-sectional study. Andrology, 2021, 9(1): 238-244.
14.	Kim J, Kim J, Kwak MJ, et al. Genetic prediction of type 2 diabetes using deep neural network. Clin Genet, 2018, 93(4): 822-829.
15.	宗菁, 強德仁, 孔曉玲, 等. 武進農村地區人群靜息心率與 2 型糖尿病發病風險的隊列研究. 現代預防醫學, 2021, 48(22): 4104-4107.
16.	Julius S, Gudbrandsson T, Jamerson K, et al. The interconnection between sympathetics, microcirculation, and insulin resistance in hypertension. Blood Press, 1992, 1(1): 9-19.
17.	Basu S, Stuckler D, McKee M, et al. Nutritional determinants of worldwide diabetes: an econometric study of food markets and diabetes prevalence in 173 countries. Public Health Nutr, 2013, 16(1): 179-186.
18.	de Oliveira CM, Viater Tureck L, Alvares D, et al. Relationship between marital status and incidence of type 2 diabetes mellitus in a Brazilian rural population: the Baependi heart study. PLoS One, 2020, 15(8): e0236869.
19.	鄔錫波, 鄧旭, 韓紅霞, 等. 寧波市中老年人糖尿病的風險預測模型. 慢性病學雜志, 2022, 23(9): 1335-1341.
20.	Darnton-Hill I, Nishida C, James WP. A life course approach to diet, nutrition and the prevention of chronic diseases. Public Health Nutr, 2004, 7(1A): 101-121.
21.	韓嘯, 應長江, 周曉燕, 等. 2 型糖尿病患者單核細胞/高密度脂蛋白膽固醇比值與胰島素抵抗關系的研究. 中國糖尿病雜志, 2021, 29(9): 659-664.
22.	徐幽瓊, 陸璐, 曹祥玉, 等. 福州市社區居民膳食模式與糖尿病患病相關性研究. 中國慢性病預防與控制, 2022, 30(7): 497-501.
23.	劉凌霞, 成景山, 莫芳梅, 等. 148 例老年 2 型糖尿病患者血糖控制效果及影響因素研究. 右江醫學, 2021, 49(7): 510-514.
24.	張雅茜, 張杏花, 范柳媚. 白云區成人糖尿病知識知曉率及其影響因素分析. 慢性病學雜志, 2022, 23(2): 188-190.
25.	李賢, 王惠群, 吳延莉, 等. 貴州省 2 型糖尿病和糖尿病前期流行現狀及影響因素分析. 中國慢性病預防與控制, 2021, 29(5): 373-376.
26.	Martín-Peláez S, Fito M, Castaner O. Mediterranean diet effects on type 2 diabetes prevention, disease progression, and related mechanisms. A review. Nutrients, 2020, 12(8): 2236.
27.	Kolb H, Martin S. Environmental/lifestyle factors in the pathogenesis and prevention of type 2 diabetes. BMC Med, 2017, 15(1): 131.
28.	宋玲, 孟俊華, 王玉玲. 糖尿病患者周圍神經病變的關聯因素分析. 解放軍醫學院學報, 2022, 43(4): 426-430.

1. 付俊玲, 穆志靜, 孫麗娜, 等. 住院 2 型糖尿病患者視網膜病變的危險因素及膽紅素水平分析. 中華糖尿病雜志, 2022, 14(9): 947-953.
2. 姬超琴, 王文絹. 成本效果分析在 2 型糖尿病心血管疾病危險因素控制中的應用. 中國慢性病預防與控制, 2022, 30(10): 797-801.
3. Deng Y, Xiao H, Xu J, et al. Prediction model of PSO-BP neural network on coliform amount in special food. Saudi J Biol Sci, 2019, 26(6): 1154-1160.
4. Liu X, Liu Z, Liang Z, et al. PSO-BP neural network-based strain prediction of wind turbine blades. Materials (Basel), 2019, 12(12): 1889.
5. Mayfield J. Diagnosis and classification of diabetes mellitus: new criteria. Am Fam Physician, 1998, 58(6): 1355-1362, 1369-1370.
6. Han IJ, Yuan TF, Lee JY, et al. Learned Prediction of Compressive Strength of GGBFS Concrete Using Hybrid Artificial Neural Network Models. Materials (Basel), 2019, 12(22): 3708.
7. 李大虎, 李秋科, 王文才, 等. 基于 MIV 特征選擇與 PSO-BP 神經網絡的煤炭發熱量預測. 煤炭工程, 2020, 52(11): 154-160.
8. 侯哲, 仝衛國. 基于 PSO-BP 神經網絡的脫硫制漿系統能耗建模. 自動化與儀器儀表, 2022(9): 26-29.
9. 路思恒, 尹紅. 基于 BP 神經網絡的居民消費水平的預測模型——以安徽省為例. 現代電子技術, 2022, 45(21): 83-87.
10. 郭中小, 宋一凡, 廖梓龍, 等. 基于 MIV 的遺傳神經網絡徑流預報模型. 人民黃河, 2014, 36(10): 33-35.
11. Wu Y, Ding Y, Tanaka Y, et al. Risk factors contributing to type 2 diabetes and recent advances in the treatment and prevention. Int J Med Sci, 2014, 11(11): 1185-1200.
12. 盧潮. 中山市住院 2 型糖尿病患者慢性并發癥發病情況調查及危險因素分析. 糖尿病新世界, 2022, 25(6): 38-41.
13. Yilmaz M, Karaaslan M, Tonyali S, et al. Triglyceride-glucose index (TyG) is associated with erectile dysfunction: a cross-sectional study. Andrology, 2021, 9(1): 238-244.
14. Kim J, Kim J, Kwak MJ, et al. Genetic prediction of type 2 diabetes using deep neural network. Clin Genet, 2018, 93(4): 822-829.
15. 宗菁, 強德仁, 孔曉玲, 等. 武進農村地區人群靜息心率與 2 型糖尿病發病風險的隊列研究. 現代預防醫學, 2021, 48(22): 4104-4107.
16. Julius S, Gudbrandsson T, Jamerson K, et al. The interconnection between sympathetics, microcirculation, and insulin resistance in hypertension. Blood Press, 1992, 1(1): 9-19.
17. Basu S, Stuckler D, McKee M, et al. Nutritional determinants of worldwide diabetes: an econometric study of food markets and diabetes prevalence in 173 countries. Public Health Nutr, 2013, 16(1): 179-186.
18. de Oliveira CM, Viater Tureck L, Alvares D, et al. Relationship between marital status and incidence of type 2 diabetes mellitus in a Brazilian rural population: the Baependi heart study. PLoS One, 2020, 15(8): e0236869.
19. 鄔錫波, 鄧旭, 韓紅霞, 等. 寧波市中老年人糖尿病的風險預測模型. 慢性病學雜志, 2022, 23(9): 1335-1341.
20. Darnton-Hill I, Nishida C, James WP. A life course approach to diet, nutrition and the prevention of chronic diseases. Public Health Nutr, 2004, 7(1A): 101-121.
21. 韓嘯, 應長江, 周曉燕, 等. 2 型糖尿病患者單核細胞/高密度脂蛋白膽固醇比值與胰島素抵抗關系的研究. 中國糖尿病雜志, 2021, 29(9): 659-664.
22. 徐幽瓊, 陸璐, 曹祥玉, 等. 福州市社區居民膳食模式與糖尿病患病相關性研究. 中國慢性病預防與控制, 2022, 30(7): 497-501.
23. 劉凌霞, 成景山, 莫芳梅, 等. 148 例老年 2 型糖尿病患者血糖控制效果及影響因素研究. 右江醫學, 2021, 49(7): 510-514.
24. 張雅茜, 張杏花, 范柳媚. 白云區成人糖尿病知識知曉率及其影響因素分析. 慢性病學雜志, 2022, 23(2): 188-190.
25. 李賢, 王惠群, 吳延莉, 等. 貴州省 2 型糖尿病和糖尿病前期流行現狀及影響因素分析. 中國慢性病預防與控制, 2021, 29(5): 373-376.
26. Martín-Peláez S, Fito M, Castaner O. Mediterranean diet effects on type 2 diabetes prevention, disease progression, and related mechanisms. A review. Nutrients, 2020, 12(8): 2236.
27. Kolb H, Martin S. Environmental/lifestyle factors in the pathogenesis and prevention of type 2 diabetes. BMC Med, 2017, 15(1): 131.
28. 宋玲, 孟俊華, 王玉玲. 糖尿病患者周圍神經病變的關聯因素分析. 解放軍醫學院學報, 2022, 43(4): 426-430.

West China Medical Journal

Analysis of the risk factors and screening model establishment of type 2 diabetes mellitus based on the particle swarm optimization BP neural network

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