While Bayesian networks (BNs) offer a promising approach to discussing factors related to many diseases, little attention has been poured into chronic kidney disease with mental illness (KDMI) using BNs. This study aimed to explore the complex network relationships between KDMI and its related factors and to apply Bayesian reasoning for KDMI, providing a scientific reference for its prevention and treatment. Data was downloaded from the online open database of CHARLS 2018, a population-based longitudinal survey. Missing values were first imputed using Random Forest, followed by propensity score matching (PSM) for class balancing regarding KDMI. Elastic Net was then employed for variable selection from 18 variables. Afterwards, the remaining variables were included in BNs model construction. Structural learning of BNs was achieved using tabu algorithm and the parameter learning was conducted using maximum likelihood estimation. After PSM, 427 non-KDMI cases and 427 KDMI cases were included in this study. Elastic Net identified 11 variables significantly associated with KDMI. The BNs model comprised 12 nodes and 24 directed edges. The results suggested that diabetes, physical activity, education levels, sleep duration, social activity, self-report on health and asset were directly related factors for KDMI, whereas sex, age, residence and Internet access represented indirect factors for KDMI. BN model not only allows for the exploration of complex network relationships between related factors and KDMI, but also could enable KDMI risk prediction through Bayesian reasoning. This study suggests that BNs model holds great prospects in risk factor detection for KDMI.

中文翻译：

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使用贝叶斯网络和禁忌算法探索与慢性肾病合并精神疾病相关的因素：一项横断面研究。

虽然贝叶斯网络 (BN) 提供了一种很有前途的方法来讨论与许多疾病相关的因素，但很少有人关注使用贝叶斯网络来研究伴有精神疾病的慢性肾病 (KDMI)。本研究旨在探讨KDMI及其相关因素之间复杂的网络关系，并应用贝叶斯推理对KDMI进行推理，为其预防和治疗提供科学参考。数据是从基于人口的纵向调查 CHARLS 2018 的在线开放数据库下载的。首先使用随机森林估算缺失值，然后使用倾向得分匹配 (PSM) 来实现 KDMI 的类别平衡。然后使用 Elastic Net 从 18 个变量中进行变量选择。之后，剩余的变量被纳入 BN 模型构建中。使用禁忌算法实现BN的结构学习，并使用最大似然估计进行参数学习。PSM 后，427 例非 KDMI 病例和 427 例 KDMI 病例纳入本研究。Elastic Net 确定了 11 个与 KDMI 显着相关的变量。BN 模型由 12 个节点和 24 个有向边组成。结果表明，糖尿病、体力活动、教育水平、睡眠时间、社交活动、健康自我报告和资产是KDMI的直接相关因素，而性别、年龄、居住地和互联网使用情况是KDMI的间接因素。BN模型不仅可以探索相关因素与KDMI之间的复杂网络关系，还可以通过贝叶斯推理实现KDMI风险预测。本研究表明BNs模型在KDMI危险因素检测方面具有广阔的前景。