慢性肾脏病(CKD)是一种慢性健康状况疾病,在CKD的不同阶段,其临床表现也各不相同。所以对于慢性肾脏病患者的相关指标进行详细的分析具有重要的意义。XGBoost (Extreme Gradient Boosting)是一个优化的分布式梯度增强库,它在梯度提升(Gradient Boosting)框架下实现机器学习算法。本文提出了一种基于机器学习模型XGBoost与因果推断相结合的思想,建立了一个新模型XGBoost-CI (Extreme Gradient Boosting-Casual Inference),并利用该模型对导致慢性肾脏病的因素进行分析,最后通过与随机森林模型(Random Forest),逻辑回归模型(Logistic Regression)和LightGBM (Light Gradient Boosting Machine)三个模型进行精确度对比,证实了本文模型的有效性。Chronic kidney disease (CKD) is a chronic health condition that has a different clinical presentation at different stages of CKD. Therefore, it is of great significance to conduct a detailed analysis of the relevant indicators of patients with chronic kidney disease. XGBoost (Extreme Gradient Boosting) is an optimized distributed gradient boosting library that implements machine learning algorithms under the Gradient Boosting framework. In this paper, we propose a new model XGBoost-CI (Extreme Gradient Boosting-Casual Inference) based on the combination of machine learning model XGBoost and causal inference, and use the model to analyze the factors leading to chronic kidney disease) and Light GBM (Light Gradient Boosting Machine) models were compared to confirm the effectiveness of the proposed model.
