# Machine Learning in Medical Triage: A Predictive Model for Emergency Department Disposition
## 4 Discussion  
Based on the evaluation metrics, the GBM model shows promising performance in classifying instances into the \"ADMITTED\" and \"HOME\" classes. The low MSE and RMSE values indicate accurate predictions, supporting the model's effectiveness in minimizing prediction errors. The low log loss confirms the reliability of the model's predicted probabilities. The mean per-class error of 0.2939 demonstrates a reasonable balance in class separation, essential for applications where both classes are equally important. Moreover, the AUC and AUCPR values of 0.8256 and 0.8722, respectively, indicate the model's ability to discriminate between classes and achieve a high precision and recall. The GBM model demonstrated promising results, achieving an accuracy of 73.17% on the test data. This accuracy shows that the model correctly predicts the class for the majority of instances, though there is room for improvement. The model's performance was further evaluated using various metrics derived from the confusion matrix. The evaluation metrics revealed that the model achieved a precision of 76.32%, indicating its ability to accurately predict positive instances (ADMITTED), while a recall of 53.08% suggests the model may miss many \"ADMITTED\" cases, highlighting an area for improvement. The specificity of 72.21% indicates the model's competence in identifying true negative cases (HOME). The F1 score, which provides a balanced measure considering both precision and recall, resulted in a score of 0.6279. A balanced accuracy of 62.65% indicates moderate fairness across both classes, considering a potential dataset imbalance. These results suggest that the GBM model might be an effective tool for predicting patient disposition, either being admitted or sent home, based on the available features. However, our detailed discussion of each evaluation metric validates the robustness of  
Figure 4: Correlation heatmap of the features included in the model demonstrating the correlation amongst the features. our methodology. While the model performs well in many areas, specific aspects like recall could be enhanced. Future work might involve fine-tuning the model, exploring alternative algorithms, or incorporating additional features to improve these areas. This comprehensive analysis ensures our results are transparent, interpretable, and actionable. Further analysis and validation on different datasets are recommended to ensure the model's robustness and generalizability. We emphasize that such models should be equipped with multiple safeguards. This includes the integration of model suggestions with traditional diagnostic tools and patient data, and mandatory oversight by healthcare practitioners to validate and approve model outputs before any clinical application. Additionally, regular updates and recalibrations of the model based on ongoing feedback and emerging clinical data are necessary in order to ensure its accuracy and relevance. The developers of such models should be responsible for maintaining transparency in the model's function and actively working on enhancing its predictive accuracy. Special attention should be given to improving the sensitivity of the model to reduce the risk of false negatives significantly. Additionally, explainability features should be incorporated to ensure that healthcare providers understand the rationale behind model predictions, fostering trust and facilitating more informed decision-making. These steps are crucial for embedding such models into the clinical workflow responsibly and ethically, ensuring that they serve as a reliable aid in the high-stakes environment of emergency medicine. One particularly interesting type of further analysis refers to actually evaluating the quality of the medical decision, taking into account the re-admission occurrence (for example). The temporal dimension of patient admittance, when considered in parallel with some degree of retrospective analysis of previous diagnoses and treatments, may lead to a wealth of new information.