Machine Learning Risk Prediction

Benefits

Preparation

Comparisons

Highlighted claims

• Eight supervised learning classification algorithms were evaluated, comprising three linear models and five non-linear models. — Development and validation of a machine learning model for prediction of 1-year mortality following ST-elevation myocardial infarction: a retrospective cohort study
• Hyperparameters were optimised with fivefold cross-validation, using area under the curve as the scoring metric. — Development and validation of a machine learning model for prediction of 1-year mortality following ST-elevation myocardial infarction: a retrospective cohort study
• The dataset was split 80/20 into training and test sets, yielding approximately 1490 training patients with 203 events and 373 test patients with 51 events. — Development and validation of a machine learning model for prediction of 1-year mortality following ST-elevation myocardial infarction: a retrospective cohort study
• Missing values were imputed using median values for continuous variables and mode values for categorical variables, with missingness below 5% for most variables. — Development and validation of a machine learning model for prediction of 1-year mortality following ST-elevation myocardial infarction: a retrospective cohort study
• Machine learning methods may improve risk prediction by identifying complex patterns in large datasets that traditional statistical approaches may not capture. — Development and validation of a machine learning model for prediction of 1-year mortality following ST-elevation myocardial infarction: a retrospective cohort study