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Overcrowding in EDs has been viewed globally as a chronic health challenge. It is directly related to the increased use of EDs for non-urgent issues, leading to increased complications, long waiting times, a higher death rate, or delayed intervention of those more acutely ill. This study aims to develop Machine Learning models to differentiate immediate medical needs from unnecessary ED visits. A Decision Tree, Random Forest, AdaBoost, and XGBoost models were built and evaluated on real-life data. XGBoost achieved the best accuracy and F1-score.
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