首页|基于粒子群优化算法BP神经网络的2型糖尿病危险因素分析及筛查模型构建

基于粒子群优化算法BP神经网络的2型糖尿病危险因素分析及筛查模型构建

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目的 基于粒子群优化算法(particle swarm optimization,PSO)BP神经网络,分析2型糖尿病的危险因素,并构建2型糖尿病筛查模型.方法 选择2021年7月-2022年8月广东医科大学附属医院和广东医科大学附属第二医院内分泌科2型糖尿病住院患者为病例组,以同期广东医科大学附属医院健康管理中心的健康体检人群为对照组.收集研究对象的基本信息、体检资料和实验室检查指标等进行比较分析.运用MATLAB R2021b软件分别构建PSO-BP神经网络模型、BP神经网络模型和logistic回归模型,并选出最优的2型糖尿病筛查模型.在此基础上,采用平均影响值算法筛选2型糖尿病发病的危险因素.结果 病例组共纳入患者1 053例,对照组共纳入健康体检人群914例.除食盐类型、共病家族史、体质量指数、总胆固醇、低密度脂蛋白胆固醇、主食摄入外(P＞0.05),其余指标两组比较,差异均有统计学意义(P＜0.05).PSO-BP神经网络模型的整体筛查性能优于BP神经网络模型和logistic回归模型.基于PSO-BP神经网络模型,平均影响值算法因素筛选结果显示2型糖尿病发病的危险因素为空腹血糖、心率、年龄、腰臂比和婚姻状况,保护因素为高密度脂蛋白胆固醇、蔬菜摄入、居住地、文化程度、水果摄入和肉类摄入.结论 2型糖尿病的影响因素较多,应重点关注高危人群并定期开展疾病筛查,减少2型糖尿病的发病风险.PSO-BP神经网络筛查模型的性能最佳,未来可推广至其他疾病的早期筛查及诊断.
Analysis of the risk factors and screening model establishment of type 2 diabetes mellitus based on the particle swarm optimization BP neural network
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.

Type 2 diabetes mellitusparticle swarm optimizationbp neural networkmean impact valuerisk factors

王仕鸿、陈永泽、麦振华、谭茜蔚、杨子华、王爽、赵乐、马勇、黄翠怡、孔丹莉、丁元林、于海兵

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广东医科大学公共卫生学院(广东东莞 523808)

2型糖尿病 粒子群优化算法 BP神经网络 平均影响值算法 危险因素

广东省医学科研基金广东省基础与应用基础研究基金自然科学基金广东省基础与应用基础研究基金区域联合基金2022年东莞市社会发展科技项目湛江市科技发展专项资金竞争性分配项目广东医科大学学科建设项目大学生创新创业训练计划项目广东医科大学校级大学生创新创业训练计划项目广东医科大学校级大学生创新创业训练计划项目

A20213952022A15150124072020B1515120021202218009056422020A010314SG21276PS202210571088GDMU2021138GDMU2021112

2024

10.7507/1002-0179.202311033

华西医学
四川大学华西医院

华西医学

CSTPCD
影响因子：0.744
ISSN：1002-0179
年,卷(期)：2024.39(2)
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