# Machine-Learning-Based COVID-19 and Dyspnoea Prediction Systems for the Emergency Department
## 3 Results and Discussion  
Several studies and reviews highlighted the importance and the role of AI-based medical instruments to aid physicians and engage limited resources [12, 21, 22]. Researchers are producing novel methodologies to define biomarkers and process signals, reshaping how we address clinical tools [9, 10, 20, 21, 22]. Here, we propose two diagnostic tools for COVID-19 detection and oxygen therapy prediction, namely, the need for ventilation support due to lung involvement. The models chosen for the two classification tasks steadily seized convergence throughout optimization concerning the hyperparameters displayed in Table 2. We assessed the gathered results by evaluating accuracy, precision, sensitivity--i.e., recall--and the F1-score [28]. The first metric informs the reader about how good we are at diagnosing the absence of SARS-CoV-2, whilst the latter is the degree of accuracy we have over an unbalanced dataset, measured employing precision and recall. Indeed, the consequences of incorrectly diagnosing a patient as healthy are the inappropriate lack of treatments and cross-contamination among subjects presenting other pathologies. Considering both clustering scenarios, we found area under the curve levels (Figures 5 and 6) exceeding 93%. At the same time, we measured 96% recall when considering COVID-19 detection. We found an overall F1-score of 92% for the first task and 83% for the second one, and we also measured precision continuously above 80% (Table 3). These results are particularly worthy of notice when compared to the rRT-PCR test. Indeed, the nasopharyngeal swab attains 73.3% sensitivity (95% CI 68.1-78.0%). Furthermore, currently, no metric exists to determine if the ED will need other resources [40]. Therefore, we provided a valuable tool to wisely manage the hospital's limited resources by predicting whether the considered patient will need CPAP or invasive ventilation. Physicians usually make decisions by looking at the patients' continuous vital signs, whilst we predict future needs concerning a particular time, namely when the patient arrives at the ED. This process implies that the clinical picture we consider could change abruptly at any moment. Nonetheless, we measured an 83% F1-score and ROC-AUC values above 90%, reported in Figure 5.  
Figure 5: ROC curve of the SVM model for oxygen therapy classification (on the **left**) and the ROC curve of the RF model for oxygen therapy classification (on the **right**). Comparing our classification performance with our colleagues, which is reported in Table 4[23; 24; 41; 26], we not only managed to improve their results while considering a more significant number of features, but also handled a smaller and particularly unbalanced dataset. Indeed, concerning COVID-19 detection, we reached 96% of recall, whilst others could exceed 90% only using a three-way model [36]. Namely, a model abstains from prediction when the confidence score is below 75%. Other researchers reached 95.9% sensitivity with 41.7% specificity [41]. Their model represents a valuable screening tool to rule out COVID-19 infection. However, such a low specificity can be dangerous in facing an infectious disease. Indeed, it is more important to identify positive patients and isolate them than to rule out negative ones. On the one hand, we collected a dataset whose features represent the daily clinical scenario that the physicians are given. Namely, we inserted the quantitative LUS examination together with blood and ABG tests. We believe that this process allowed the robust classification performance obtained. On the other hand, our dataset is smaller than that of our colleagues [23; 26; 36; 41], although we managed to handle both its size and class imbalances, and the LUS examination requires trained personnel to be performed.