Key result
A support vector machine model achieved the best performance in predicting one-year hospital readmission for heart failure patients with an AUC score of 0.602.
Why the study?
Heart failure readmission rates remain high despite being potentially preventable, and few predictive models incorporate cost considerations.
Which machine learning algorithm best predicts one-year hospital readmission in heart failure patients when incorporating cost considerations?
Cohort
Which machine learning algorithm best predicts one-year hospital readmission in heart failure patients when incorporating cost considerations?
A support vector machine algorithm outperformed other machine learning models in predicting one-year hospital readmission for heart failure patients, achieving an AUC of 0.602.
Cost-integrated ML models may refine HF readmission prediction; leaves open prospective validation before clinical use.
Readmission rates for heart failure patients remain high but it is potentially preventable. Many predictive models have been developed over the years to identify heart failure patients who are at high risk of readmission but only a few of them incorporate cost considerations. The goal of this study is to compare the performance of four machine learning algorithms in predicting the readmission of heart failure patients with cost consideration. We also aim to identify the risk factors associated with a patient’s readmission within one year of a retrospective cohort study. The best model selection was found after four machine-learning methods were tested; these include logistic regression, support vector machine, random forest and neural network. The study found a support vector machine to have the best prediction performance with an AUC score of 0.602. The model showed twelve (12) predictors that are significantly associated with the identification of heart failure patients at high risk of readmission.
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Landicho et al. (2020) conducted a cohort in Heart failure. Support vector machine vs. Logistic regression, random forest, and neural network was evaluated on Prediction of hospital readmission within one year (AUC 0.602). A support vector machine model achieved the best performance in predicting one-year hospital readmission for heart failure patients with an AUC score of 0.602.
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