Ensemble Machine Learning Models for Predicting Patients With High Usage: Model Validation and Economic Impact Analysis

BACKGROUND: Machine learning models are increasingly used to predict patients at risk of high health care usage for targeted interventions. OBJECTIVE: This study aimed to evaluate the predictive performance of multiclass ensemble models across different levels of health care usage and assess their potential application through real-world economic impact analysis. METHODS: A total of 4 previously developed binary classification models (base learners)-boosted trees, multivariate adaptive regression splines, multilayer perceptron, and logistic regression-were extended using a stacking ensemble approach. These base learner models generated individual-level predicted probabilities, which were used as inputs to build multiclass prediction models forecasting usage across defined strata: length of stay (LOS) of