# Machine Learning-Based Model for Emergency Department Disposition at a Public Hospital
## 5 Discussion  
Ongoing attempts are being made to improve and integrate clinical decision support systems that can present objective criteria for healthcare professionals. In the present study, we evaluated seven ML algorithms for predicting hospital emergency outcomes. The results revealed that RF, an ensemble-based algorithm, achieved the highest AUC score among the models tested. This suggests that RF's ensemble learning approach might be particularly effective in capturing complex relationships within ED data. We also demonstrated that ML models could be developed to support decision-making processes in the ED. Specifically, utilizing 34 features, the RF model achieved an AUC of 0.958 in predicting four distinct outcomes in our dataset. Table 4 presents the comparison machine learning models for emergency department prediction. [PERSON] et al. [13] found 14 out of 367 articles that met their criteria in their review of models used to identify patients hospitalized in the ED. Their paper stated that logistic regression was the most used prediction model and that it achieved an AUC value of 0.75-0.92. The AUC value we found for the LR in our study (0.694) aligns with these findings. The number of variables used in the studies included in this review varied between 6 and 21. We think that the 34 variables used in our study strengthens our findings. These papers evaluated the variables under six categories (demographic data, triage data, clinical and laboratory findings, medical history, medicine, and others). Since the medical history and medicine used could not be obtained from the records in our study, these variables were not included. Not being able to obtain medical history is a weakness of our study. AI models can contribute to increasing the quality of care in EDs and alleviate the burden on healthcare systems. In their study, conducted with 3204 ED patients, [PERSON] \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **The Best ML Algorithm** & **Performance Metric (AUC)** & **Number of Features** & **Outcome Prediction Type** & **Applying Feature Selection** \\ \hline \hline [PERSON] et al. [13] & LR & [0.75–0.92] & [6–21] & To identify patient hospitalizations & - \\ \hline [PERSON] et al. [14] & RF & 0.789 & 18 & To predict hospital admissions & No \\ \hline [PERSON] et al. [15] & A binary classifier & 0.94 & - & For early prediction of hospital admissions & No \\ \hline [PERSON] et al. [16] & LR & 0.825 & 8 & To predict hospital admissions & No \\ \hline [PERSON] et al. [17] & XGBost & 0.84 & 5 & Hospital modeling & No \\ \hline [PERSON] et al. [18] & XGBost & 0.92 & 10 & To predict hospital admissions & No \\ \hline [PERSON] et al. [19] & GBM & 0.85 & 13 & To predict hospital admissions & Yes \\ \hline [PERSON] and [PERSON] [20] & RF & 0.91 & 10 & To accurately predict emergency department outcomes & Yes \\ \hline [PERSON] et al. [21] & GBM & 0.89 & 9 & To predict hospital admissions & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of machine learning models for emergency department prediction. et al. [14] stated, considering the weighted mean values, that the RF performed slightly better than other models in terms of F-measure and ROC with values of 0.723 and 0.789, respectively. Additionally, they stated that the F-measure and ROC value intervals of all of the eight algorithms were respectively 0.663-0.723 and 0.731-0.789, and, as stated before, these can be deemed acceptable. Similarly, in our study, RF seemed to be the best modeling method. The higher values of F-measure and AUC scores in the models of our study might have been due to the number of patients included in the research and their distribution into groups. Our study justifies the current common opinion that AI can positively influence the future of emergency medicine. In their study, [PERSON] et al.