Hospital readmission prediction based on improved feature selection using grey relational analysis and LASSO

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.