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.
Exploring Fairness In Machine Learning For International Development - Module 3: Pedagogical Framework for Addressing Ethical Challenges - Fairness Criteria
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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.