Predictive modeling of hospital readmissions using metaheuristics and data mining
Risk predictions of hospital readmission for heart failure patients are investigated. RBFNN, RF and PSOSVM models are used to predict patient readmission risk. The PSOSVM achieves 78.4% on overall accuracy and 97.3% on sensitivity. The PSOSVM outperforms over traditional prediction models, including LACE scores.
hospital readmission risk prediction neural networks support vector machine particle swarm optimization