Joint imbalanced classification and feature selection for hospital readmissions

Hospital readmission is one of the most important service quality measures. Recently, numerous risk assessment models have been proposed to address the hospital readmission problem. However, poor understanding of the class-imbalance hospital readmission data still challenges the development of accurate predictive models. To overcome the issue, a new risk prediction method termed joint imbalanced classification and feature selection (JICFS) is proposed for handling such a problem. To be specific, we construct the loss function within the large margin framework, in which the sample weight is involved to deal with the class imbalanced problem. Based on this, we design an optimization objective function involving l(1)-norm regularization for improving the performance, and an iterative scheme is proposed to solve the optimization problem, thereby achieving feature selection to improve the performance. Finally, experimental results on six real-world hospital readmission datasets demonstrate that the proposed algorithm has the advantage compared with some state-of-the-art methods. (C) 2020 Elsevier B.V. All rights reserved.