基于ADE-Stacking的心力衰竭非计划性再入院风险预测模型

随着人口老龄化加剧，心力衰竭发病率升高，心衰患者的非计划性再入院问题导致患者生存质量降低、医疗成本升高的情况日益严重，因此成为了一个亟待解决的问题。本文针对再入院风险预测问题，提出一种基于ADE-Stacking的心衰患者非计划性再入院风险预测模型，这一模型主要由集成学习算法模型构建与参数优化2部分构成，集成学习算法可以结合多个弱分类器的优势，使模型具有更好的泛化性和准确率，参数优化部分采用自适应收缩因子F改进的差分进化算法寻优，以提高参数寻优性能。使用心力衰竭再入院病人数据集对模型进行训练与测试，结果显示本文所提出的模型优于风险预测模型常用的随机森林、XGBoost、支持向量机等其他机器学习算法。

Risk Prediction Model of Heart Failure Unplanned Readmission Based on ADE-Stacking

With the increasing aging of the population, the incidence of heart failure has increased, and the problem of unplanned readmission of patients with heart failure has led to a decrease in the quality of life of patients and an increase in medical costs. Therefore, it has become an urgent problem to be solved. Aiming at the problem of readmission risk prediction, this paper proposes an unplanned readmission risk prediction model for heart failure patients based on ADE-Stacking. This model is mainly composed of two parts: integrated learning algorithm model construction and parameter optimization. The integrated learning algorithm can be combined with multiple parts. The advantages of a weak classifier make the model have better generalization and accuracy. The parameter optimization part uses the adaptive shrinkage factor F to improve the differential evolution algorithm to improve the parameter optimization performance. The model is trained and tested using the heart failure readmission patient data set. The results show that the proposed model is better than other machine learning algorithms such as random forest, XGBoost, support vector machine and other commonly used risk prediction models.