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;
KONG Danli, Email: kdlgdmu@gdmu.edu.cn; DING Yuanlin, Email: gdmcsbd@163.com; YU Haibing, Email: hby616688@163.com
Type 2 diabetes mellitus; particle swarm optimization; bp neural network; mean impact value; risk factors
10.7507/1002-0179.202311033
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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