# Machine Learning-Based Model for Emergency Department Disposition at a Public Hospital
## 5 Discussion
[15] used six different ML algorithms (decision trees, support vector machines, RF, naive bias classifier, gradient boosting, and deep neural networks) for early prediction of hospital admissions of patients who were presented to the ED, and stated that the algorithms performed perfectly in predicting hospital admissions within the first 30 min. In their modeling study, conducted using ROC analysis, [PERSON] et al. [16] analyzed 335K patients and utilized eight variables in the models. The area under the curve in the ROC curve was 0.825 (95% CI 0.824-0.827), which is deemed a good score. In our study, the area under the curve according to the ROC analyses was 0.842 after all the models were averaged, which complies with their study. Old age, increasing triage acuity, and arrival type through special patient transfer methods were the factors that best predicted the hospital admissions [16]. The hospital admission possibility of patients transferred by special patient transfer vehicles was higher than those who came in on foot, while those arriving by state ambulance was much lower. While age and increasing triage acuity were evaluated as good indicators in our study, the type of arrival was not deemed a good indicator. This might have been caused by regional differences [16]. In the study by [PERSON] et al. [17], who used demographic characteristics, emergency, application complaints, severity of disease, and accompanying diseases as common variables, the models were evaluated at 30 min and 2 h after triage. The AUC values were RF 0.80 and XGBoost 0.84 in triage, and 0.86 and 0.86 at 30 min and 2 h, respectively. Although our study was not evaluated hourly, the highest AUC values were obtained from the RF and XGBoost models. It is stated in the study conducted by [PERSON] et al. that time does not enhance the performance of the models. The study by [PERSON] et al. showed that ML models had an excellent but similar predictive performance as the logistic regression model for predicting hospital admissions. On the other hand, our study showed that models can predict much better compared to LR. Using more variables might have led to that result. [PERSON] et al. [18] reported AUC values of 0.87 for LR and XGBoost and 0.91 and 0.92 for all variables, respectively, in hospitalization modeling with triage variables. They found the XGBoost model to be better than LR in prediction performance, a finding that aligns with our study. It is seen in their study that adding previous information drastically improved the prediction performance compared to only using triage information. While variables such as ESI levels, outpatient treatment, demographic characteristics, and hospital usage statistics provided high-level information, variables such as age, gender, marital status, previous operation procedures, and hospital arrival type were deemed to provide less information. In our study, while variables such as age, gender, and triage score were statistically significant, hospital arrival type was not significant. This difference might have arisen from the generalization of the models used. [PERSON] et al. [19], in their study, in which they used LR, decision trees, and gradient-boosted machine models, stated that GBM had the best performance; however, decision tree and LR models only showed a slightly greater improvement; therefore, it revealed GBM as the potential candidate to apply three models. There are very important points to that study published in 2018 [18,19]. Firstly, the inclusion of additional data alongside triage data can improve accuracy; secondly, textual data can be incorporated into ML modeling; and,lastly, the use of ML algorithms can improve accuracy. All three of them were used in our study and were proved to be accurate by us [19]. [PERSON] and [PERSON] [20] explored RF, AutoML, LR, Neural Network, and GBM to support emergency department operations, focusing on predicting patient outcomes.