TY - JOUR
T1 - Joint imbalanced classification and feature selection for hospital readmissions
AU - Du, Guodong
AU - Zhang, Jia
AU - Luo, Zhiming
AU - Ma, Fenglong
AU - Ma, Lei
AU - Li, Shaozi
N1 - Funding Information:
This work is supported by the National Nature Science Foundation of China (No. 61876159 , No. 61806172 & No. U1705286 ), the National Key Research and Development Program of China (No. 2018YFC0831402 ), the China Postdoctoral Science Foundation (No. 2019M652257 ), Fujian Province 2011 Collaborative Innovation Center of TCM Health Management .
Funding Information:
This work is supported by the National Nature Science Foundation of China (No. 61876159, No. 61806172 & No. U1705286), the National Key Research and Development Program of China (No.2018YFC0831402), the China Postdoctoral Science Foundation (No. 2019M652257), Fujian Province 2011 Collaborative Innovation Center of TCM Health Management.
Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/7/20
Y1 - 2020/7/20
N2 - 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 ℓ1-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.
AB - 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 ℓ1-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.
UR - http://www.scopus.com/inward/record.url?scp=85084735570&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85084735570&partnerID=8YFLogxK
U2 - 10.1016/j.knosys.2020.106020
DO - 10.1016/j.knosys.2020.106020
M3 - Article
AN - SCOPUS:85084735570
VL - 200
JO - Knowledge-Based Systems
JF - Knowledge-Based Systems
SN - 0950-7051
M1 - 106020
ER -