Evaluation of machine learning algorithms for predicting readmission after acute myocardial infarction using routinely collected clinical data
Background The ability to predict readmission accurately after hospitalization for acute
myocardial infarction (AMI) is limited in current statistical models. Machine-learning (ML)
methods have shown improved predictive ability in various clinical contexts, but their utility in
predicting readmission after hospitalization for AMI is unknown. Methods Using detailed
clinical information collected from patients hospitalized with AMI, we evaluated 6 ML
algorithms (logistic regression, naïve Bayes, support vector machines, random forest …
myocardial infarction (AMI) is limited in current statistical models. Machine-learning (ML)
methods have shown improved predictive ability in various clinical contexts, but their utility in
predicting readmission after hospitalization for AMI is unknown. Methods Using detailed
clinical information collected from patients hospitalized with AMI, we evaluated 6 ML
algorithms (logistic regression, naïve Bayes, support vector machines, random forest …
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