Song Wenzhu, Qiu Lixia, Qing Jianbo, Zhi Wenqiang, Zha Zhijian, Hu Xueli, Qin Zhiqi, Gong Hao, Li Yafeng
School of Public Health, Shanxi Medical University, Taiyuan, China.
Department of Nephrology, Shanxi Provincial People's Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan, China.
Math Biosci Eng. 2022 Sep 19;19(12):13660-13674. doi: 10.3934/mbe.2022637.
Stroke is a major chronic non-communicable disease with high incidence, high mortality, and high recurrence. To comprehensively digest its risk factors and take some relevant measures to lower its prevalence is of great significance. This study aimed to employ Bayesian Network (BN) model with Max-Min Hill-Climbing (MMHC) algorithm to explore the risk factors for stroke. From April 2019 to November 2019, Shanxi Provincial People's Hospital conducted opportunistic screening for stroke in ten rural areas in Shanxi Province. First, we employed propensity score matching (PSM) for class balancing for stroke. Afterwards, we used Chi-square testing and Logistic regression model to conduct a preliminary analysis of risk factors for stroke. Statistically significant variables were incorporated into BN model construction. BN structure learning was achieved using MMHC algorithm, and its parameter learning was achieved with Maximum Likelihood Estimation. After PSM, 748 non-stroke cases and 748 stroke cases were included in this study. BN was built with 10 nodes and 12 directed edges. The results suggested that age, fasting plasma glucose, systolic blood pressure, and family history of stroke constitute direct risk factors for stroke, whereas sex, educational levels, high density lipoprotein cholesterol, diastolic blood pressure, and urinary albumin-to-creatinine ratio represent indirect risk factors for stroke. BN model with MMHC algorithm not only allows for a complicated network relationship between risk factors and stroke, but also could achieve stroke risk prediction through Bayesian reasoning, outshining traditional Logistic regression model. This study suggests that BN model boasts great prospects in risk factor detection for stroke.
中风是一种主要的慢性非传染性疾病，具有高发病率、高死亡率和高复发率。全面剖析其危险因素并采取相关措施降低其患病率具有重要意义。本研究旨在采用带有最大最小爬山法（MMHC）算法的贝叶斯网络（BN）模型来探究中风的危险因素。2019年4月至2019年11月，山西省人民医院在山西省十个农村地区开展了中风机会性筛查。首先，我们采用倾向得分匹配法（PSM）对中风进行类别平衡。之后，我们使用卡方检验和逻辑回归模型对中风的危险因素进行初步分析。将具有统计学意义的变量纳入BN模型构建。使用MMHC算法实现BN结构学习，并通过最大似然估计实现其参数学习。经过PSM后，本研究纳入了748例非中风病例和748例中风病例。构建的BN有10个节点和12条有向边。结果表明，年龄、空腹血糖、收缩压和中风家族史构成中风的直接危险因素，而性别、教育水平、高密度脂蛋白胆固醇、舒张压和尿白蛋白与肌酐比值代表中风的间接危险因素。带有MMHC算法的BN模型不仅能够呈现危险因素与中风之间复杂的网络关系，还能通过贝叶斯推理实现中风风险预测，优于传统的逻辑回归模型。本研究表明，BN模型在中风危险因素检测方面具有广阔前景。
