基于ADASYN-IFA-Stacking的再入院患者风险预测方法

在医疗领域,非计划性再入院花费占据了医院总支出的很大一部分,如何降低再入院率,预防再入院情况的发生已经成为了医疗管理领域一个亟待解决的问题.在针对如何能够对再入院患者精确地进行识别这一问题进行深入研究以后,提出了一种基于ADASYN-IFA-Stacking的再入院患者风险预测方法,这一方法主要分为了不均衡数据处理、集成学习模型构建以及参数优化三个部分.不均衡数据处理解决了由于类间不均衡给模型带来的偏倚问题,集成学习模型则可以对多个子分类器的优势进行结合,使用萤火虫算法进行模型最优参数的选择能够进一步提升模型的预测性能.通过在获取到的再入院病人数据集上进行十折交叉验证实验后,结果显示所提出的预测方法要优于现在流行的支持向量机、分类与回归树、随机森林等其它机器学习方法.

Risk prediction method for readmission patients based on ADASYN-IFA-Stacking

In the medical field,unplanned readmission costs account for a large part of the total hospital expenditure.How to reduce the readmission rate and prevent the readmission occurrence has become a critical issue in the medical management field.In this paper,an in-depth study on how to accurately identify readmission patients is conducted.A method for predicting the risk of readmission patients based on ADASYN-IFA-Stacking is proposed.This method is mainly divided into three parts:unbalanced data processing,ensemble learning model construction and parameter optimization.Unbalanced data processing solves the bias problem caused by the imbalance between classes.The ensemble learning model can combine the advantages of multiple sub-classifiers.The use of firefly algorithm to select the optimal parameters of the model can further improve the predictive performance of the model.After performing the 10-fold cross-validation experiment on the acquired data of the readmitted patients,the results show that the proposed method is superior to the other popular machine learning methods such as support vector machine,classification and regression tree,random forest and so on.