Diabetes mellitus is a disease that has reached epidemic proportions globally in recent years. Consequently, the prevention and treatment of diabetes have become key social challenges. Most of the research on diabetes risk factors has focused on correlation analysis with little investigation into the causality of these risk factors. However, understanding the causality is also essential to preventing the disease. In this study, a causal discovery method for diabetes risk factors was developed based on an improved functional causal likelihood (IFCL) model. Firstly, the issue of excessive redundant and false edges in functional causal likelihood structures was resolved through the construction of an IFCL model using an adjustment threshold value. On this basis, an IFCL-based causal discovery algorithm was designed, and a simulation experiment was performed with the developed algorithm. The experimental results revealed that the causal structure generated using a dataset with a sample size of 2000 provided more information than that produced using a dataset with a sample size of 768. In addition, the causal structures obtained with the developed algorithm had fewer redundant and false edges. The following six causal relationships were identified: insulin→plasma glucose concentration, plasma glucose concentration→body mass index (BMI), triceps skin fold thickness→BMI and age, diastolic blood pressure→BMI, and number of times pregnant→age. Furthermore, the reasonableness of these causal relationships was investigated. The algorithm developed in this study enables the discovery of causal relationships among various diabetes risk factors and can serve as a reference for future causality studies on diabetes risk factors.

中文翻译：

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改进的基于功能因果可能性的糖尿病危险因素因果发现方法

糖尿病是近年来在全球范围内达到流行程度的疾病。因此，糖尿病的预防和治疗已成为关键的社会挑战。大多数关于糖尿病危险因素的研究都集中在相关性分析上，而很少对这些危险因素的因果关系进行调查。然而，了解因果关系对于预防疾病也至关重要。在本研究中，基于改进的功能因果可能性 (IFCL) 模型开发了糖尿病危险因素的因果发现方法。首先，通过使用调整阈值构建IFCL模型，解决了功能因果似然结构中冗余和错误边缘过多的问题。在此基础上，设计了一种基于IFCL的因果发现算法，并用所开发的算法进行了仿真实验。实验结果表明，使用样本量为 2000 的数据集生成的因果结构比使用样本量为 768 的数据集生成的因果结构提供的信息更多。此外，使用开发的算法获得的因果结构具有更少的冗余和错误边缘。确定了以下六个因果关系：胰岛素→血浆葡萄糖浓度、血浆葡萄糖浓度→体重指数（BMI）、肱三头肌皮褶厚度→BMI和年龄、舒张压→BMI、怀孕次数→年龄。此外，调查了这些因果关系的合理性。