Predictive modeling of hospital readmissions using metaheuristics and data mining

作者：

Highlights：

• Risk predictions of hospital readmission for heart failure patients are investigated.

• RBFNN, RF and PSO–SVM models are used to predict patient readmission risk.

• The PSO–SVM achieves 78.4% on overall accuracy and 97.3% on sensitivity.

• The PSO–SVM outperforms over traditional prediction models, including LACE scores.

摘要

•Risk predictions of hospital readmission for heart failure patients are investigated.•RBFNN, RF and PSO–SVM models are used to predict patient readmission risk.•The PSO–SVM achieves 78.4% on overall accuracy and 97.3% on sensitivity.•The PSO–SVM outperforms over traditional prediction models, including LACE scores.

论文关键词：Neural networks,Support vector machine,Particle swarm optimization,Hospital readmission,Risk prediction

论文评审过程：Available online 8 May 2015, Version of Record 2 June 2015.

论文官网地址：https://doi.org/10.1016/j.eswa.2015.04.066