# Machine-Learning-Based COVID-19 and Dyspnoea Prediction Systems for the Emergency Department
## 2 Materials and Methods
### Data Exploration
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Negative** & **Positive** & **Positive** & **[36]. 838 Negative.** \\  & **(172 Patients)** & **(271 Patients)** & **(443 Patients)** & **786 Positive** \\ \hline  & 25–75 Percentile & 25–75 Percentile & 25–75 Percentile & 25–75 Percentile \\  & (Median) & (Median) & (Median) & (Median) \\ _Age (years)_ & 37.0–675 (54) & 51.0–75.0 (63) & 47.0–73.0 (60) & 48.0–77.0 (62) \\ _Systolic Up (mmHg)_ & 125.0–150.0 (135) & 115.5–144.0 (130) & 120.0–145.0 (130) & \\ _Diastolic Up (mmHg)_ & 70.0–90.0 (80) & 70.0–85.8 (80) & 70.0–90.0 (80) & \\ _Mean Up (mmHg)_ & 91.6–106.7 (100) & 88.3–104.3 (95) & 90.0–105.0 (96.7) & \\ _Respiratory rate_ & 16.0–22.0 (20) & 16.0–26.0 (20) & 16.0–24.0 (20) & \\ _Oxygen saturation (\%)_ & 94.0–98.0 (97) & 90.0–97.0 (94) & 91.0–98.0 (95) & \\ _Body temperature (\({}^{\circ}\)C)_ & 36.2–37.6 (36.7) & 36.5–38.0 (37.1) & 36.3–37.9 (37) & \\ _Hemoglobin (g/L)_ & 12.2–14.9 (13.5) & 12.8–14.9 (13.9) & 12.6–14.9 (13.7) & 11.8–14.6 (13.4) \\ _White blood cell (10\({}^{9}\)/L)_ & 6.3–11.5 (8.2) & 4.8–8.1 (6.3) & 5.2–9.2 (6.9) & 5.7–10.7 (7.8) \\ _Neutrophils (10\({}^{9}\)/L)_ & 3.7–9.2 (5.6) & 3.2–6.6 (4.6) & 3.4–7.1 (4.9) & 3.7–8.1 (5.3) \\ _Lymphocytes (10\({}^{9}\)/L)_ & 0.9–22.2 (1.6) & 0.6–1.1 (0.8) & 0.7–1.6 (1) & 0.8–1.7 (1.2) \\ _Plattelts (10\({}^{9}\)/L)_ & 179.5–272.5 (224.5) & 146.0–239.0 (184) & 157.0–256.8 (204) & 171.0–282.0 (222.3) \\ _C-reactive proteins (mg/L)_ & 0.1–10.5 (1.3) & 2.6–15.2 (7.9) & 0.9–14.4 (5.3) & 0.6–10.2 (3.9) \\ _Lactate dehydrogenase (U/L)_ & 182.0–290.0 (222) & 243.5–428.0 (326) & 211.3–399.8 (286) & 218.4–388.8 (278) \\ _Creatine phosphokinase (U/L)_ & 51.0–143.0 (86) & 68.0–293.5 (113) & 62.0–217.8 (99) & 53.0–162.5 (86) \\ _PH_ & 7.4–7.5 (7.4) & 7.4–7.5 (7.5) & 7.4–7.5 (7.4) & \\ _O\({}_{2}\) pp (mmHg)_ & 69.5–102.0 (84.8) & 58.7–82.6 (86.7) & 62.6–92.8 (74.1) & \\ _CO\({}_{2}\) pp (mmHg)_ & 31.4–38.6 (35.7) & 29.6–35.8 (33) & 30.3–37.0 (33.9) & \\ _P_H_O\({}_{2}\)/_Fi_O\({}_{2}\) & 317.6–462.9 (392.2) & 226.4–352.7 (299.5) & 256.1–405.8 (323.8) & \\ _A-a-g of O\({}_{2}\) (mmHg)_ & 9.6–42.6 (22.5) & 33.6–93.1 (47.3) & 20.8–60.9 (40.4) & \\ _LUS Score_ & 0.0–7.5 (2) & 6.0–16.0 (11) & 2.0–13.0 (7) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dataset statistics. Bp stands for blood pressure, pp means partial pressure, and A-a-g is the arterial–alveolar gradient. to maintain the 95% statistical variance in our dataset. We report that to keep 95% of information while reducing the number of input features, we should keep 48 principal components instead of 58 (Figure 4). The reduction was not significant enough to justify a further level of complexity. Therefore, the models described in the next section do not perform any feature selection process prior to prediction. Additionally, physicians asked for a fast answer. Hence, we kept all the input features without a further pre-processing step beside the classical ones, which comprise feature rescaling and cleaning. Figure 4: The number of components needed to explain 95% of statistical variance. Figure 3: Scatter plot matrices for COVID-19 and oxygen therapy prediction. On the left, orange points indicate positive patients, whereas blue points indicate negative ones. On the right, orange points indicate patients who needed CPAP or invasive ventilation, whereas blue points indicate patients who needed either an oxygen mask, nasal cannula, or no oxygen therapy.