A retrospective cohort study trains LASSO, random forest, XGBoost, and neural network models on EHR data to predict MASLD, selects the interpretable LASSO with top-10 features, and applies equal-opportunity postprocessing to improve fairness across subgroups.
http://jmlr.org/papers/v18/16-365
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Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease using Machine Learning Methods: A Retrospective Cohort Study
A retrospective cohort study trains LASSO, random forest, XGBoost, and neural network models on EHR data to predict MASLD, selects the interpretable LASSO with top-10 features, and applies equal-opportunity postprocessing to improve fairness across subgroups.