Purpose

This paper develops a robust hospital readmission prediction framework by combining the feature selection algorithm and machine learning (ML) classifiers. The improved feature selection is proposed by considering the uncertainty in patient's attributes that leads to the output variable.

Design/methodology/approach

First, data preprocessing is conducted which includes how raw data is managed. Second, the impactful features are selected through feature selection process. It started with calculating the relational grade of each patient towards readmission using grey relational analysis (GRA) and the grade is used as the target values for feature selection. Then, the influenced features are selected using the Least Absolute Shrinkage and Selection Operator (LASSO) method. This proposed method is termed as Grey-LASSO feature selection. The final task is the readmission prediction using ML classifiers.

Findings

The proposed method offered good performances with a minimum feature subset up to 54–65% discarded features. Multi-Layer Perceptron with Grey-LASSO gave the best performance.

Research limitations/implications

The performance of Grey-LASSO is justified in two readmission datasets. Further research is required to examine the generalisability to other datasets.

Originality/value

In designing the feature selection algorithm, the selection on influenced input variables was based on the integration of GRA and LASSO. Specifically, GRA is a part of the grey system theory, which was employed to analyse the relation between systems under uncertain conditions. The LASSO approach was adopted due to its ability for sparse data representation.

中文翻译：

---

基于灰色关联分析和LASSO改进特征选择的医院再入院预测

目的

本文通过结合特征选择算法和机器学习 (ML) 分类器开发了一个强大的医院再入院预测框架。通过考虑导致输出变量的患者属性的不确定性，提出了改进的特征选择。

设计/方法/方法

首先，进行数据预处理，包括如何管理原始数据。其次，通过特征选择过程选择有影响的特征。它首先使用灰色关联分析 (GRA) 计算每个患者再入院的相关等级，并将等级用作特征选择的目标值。然后，使用最小绝对收缩和选择算子 (LASSO) 方法选择受影响的特征。这种提出的方​​法被称为Grey-LASSO特征选择。最后的任务是使用 ML 分类器进行再入院预测。

发现

所提出的方法提供了良好的性能，最小特征子集高达 54-65% 的丢弃特征。带有 Grey-LASSO 的多层感知器提供了最佳性能。

研究限制/影响

Grey-LASSO 的性能在两个重新入院数据集中是合理的。需要进一步的研究来检查对其他数据集的普遍性。

原创性/价值

在设计特征选择算法时，对影响输入变量的选择是基于 GRA 和 LASSO 的集成。具体来说，GRA 是灰色系统理论的一部分，用于分析不确定条件下系统之间的关系。LASSO 方法因其稀疏数据表示的能力而被采用。