Recently, researchers have been applying many new machine learning techniques for predicting the risk of readmission for heart failure. Combining such techniques through ensemble schemes holds a promise to further harness predictive performance of the resulting models. To that end, we examined two ensemble schemes and applied them to a real world dataset obtained from the EMR systems for 36,245 patients from 117 hospitals across the United States over five years. Both the ensemble schemes provided similar discriminative ability (AUC: 0.70, F1-score: 0.58) that was at least equal to or better than the base models that used a single machine learning method. The clinical impact of the models using decision curve analysis showed that at a threshold predicted probability of 0.40, the ensemble models offered 20% net benefit over the treat-all and treat-none strategies.
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