## 4 Machine Learning Model
### Machine Learning Models  
As part of our analysis, we examined various fully connected MLP models and hyperparameters and found that the two MLP models, illustrated in Figure 1, possessed the highest prediction accuracy for the examined dataset. Each model is a fully connected network which consists of three layers (beside the input layer): (1) the first layer has 24 neurons, each connected to the input features described in Table 1, (2) the second layer has 16 neurons, and (3) an output layer. The output layer in the first model (MLP model A) has a single neuron which provides a binary prediction (low or high) for a COVID-19 patient's level of disease severity. For the second mode (MLP model B), the output layer has three neurons that predict severity in three levels (low, medium, or high). Each neuron in our MLP models uses the sigmoid activation function.  
The mapping of severity levels to medical conditions in each model is summarized in Table 3, based on the dataset fields shown in Table 2. For MLP model A, the low severity level includes patient hospitalization or discharging, while MLP model B embraces hospitalization only. The high severity level includes intubation and death in both models, while MLP model A also includes ICU admission. The medium severity level is available  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{1}{c}{**Field Number**} & \multicolumn{1}{c}{**Feature Field**} & \multicolumn{1}{c}{**Feature Field Values**} \\ \hline
1 & Patient sex & Female/Male \\
2 & Patient has cardiovascular disease & Yes/No \\
3 & Lag of symptoms & The lag in days from appearance of \\
4 & Entry date & symptoms to hospital admission date \\
5 & Date of symptoms & Date of first symptoms \\
6 & Patient has other background diseases & Yes/No \\
7 & Patient is obese & Yes/No \\
8 & Patient had pneumonia & Yes/No \\
9 & Patient has asthma & Yes/No \\
10 & Patient has chronic renal failure & Yes/No \\
11 & Patient has a history of smoking & Yes/No \\
12 & Patient exposed to other COVID-19 patients & Yes/No \\
13 & Patient age & Age \\
14 & Patient is pregnant & Yes/No \\
15 & Patient has diabetes & Yes/No \\
16 & Patient has Chronic obstructive pulmonary disease (COPD) & Yes/No \\
17 & Patient has immune system suppression & Yes/No \\
18 & Patient has hypertension & Yes/No \\ \hline \hline \end{tabular}
\end{table}
Table 1: COVID-19 dataset record feature fields.  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{1}{c}{**Filed Number**} & \multicolumn{1}{c}{**Field**} & \multicolumn{1}{c}{**Field Values**} \\ \hline
1 & Patient type & Outpatient/inpatient \\
2 & Patient intubated & Yes/No \\
3 & Patient admitted to ICU & Yes/No \\
4 & Patient died & Yes/No \\ \hline \hline \end{tabular}
\end{table}
Table 2: COVID-19 patient clinical outcome.  
only in MLP model B and includes patient's hospitalization or ICU admission. To avoid biasing the models' training process, an equal number of records is used for each class.  
Our experimental analysis examined a range of hyperparameters and used those that achieved the highest prediction accuracy measured on the validation dataset. Throughout the training process, the learning rate was 0.002, the batch size was 20, and the number of used batches per batch was 200 for Model A and 1000 for Model B. We used the Adam gradient-based optimizer [36] for the model back-propagation training. The list of all MLP model hyperparameters is summarized in Table 4.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Hyperparameter** & **Value** \\ \hline Number of layers & 3 \\  & Input layer: 24 \\  & Hidden layer:16 \\ Number of nodes & Output layer: \\  & Model A: 1 \\  & Model B: 3 \\ Learning rate & 0.002 \\ Optimizer & Adam \\ Batch size & 20 \\ Number of batches & Model A: 200 \\  & Model B: 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: MLP model hyperparameters.  
Figure 1: MLP machine learning models for predicting patients’ level of disease severity and deterioration.