## 1 Introduction  
On 30 January 2020, the World Health Organization (WHO) declared the COVID-19 pandemic to be a public health emergency of international concern. As of September 2021, WHO had reported more than 232 million confirmed cases of COVID-19 worldwide and more than 4.7 million deaths ([[https://covid19.who.int](https://covid19.who.int)]([https://covid19.who.int](https://covid19.who.int)), accessed on 11 October 2021). COVID-19 is characterized as a severe acute respiratory syndrome that is caused by the SARS-CoV-2 virus. While the epidemiological and clinical characteristics of COVID-19 are increasingly understood, risk factors underlying the transition from mild to severe disease in patients remain poorly understood [1]. The severity of COVID-19 disease varies considerably among patients, ranging from asymptomatic infection to mild upper-respiratory tract illness, to severe viral pneumonia with acute respiratory distress, respiratory failure, and thromboembolic events that can lead to death [1]. Therefore, evaluating a patient's likely severity of illness remains an important concern for both clinicians and policy makers. Such an understanding would assist practitioners in performing rapid, early, and effective triage, help conserve strained hospital resources, and deeper our understanding of the disease [2]. Evidence obtained before the introduction of the COVID-19 vaccine suggests that 6-10% of infected patients are likely to become critically ill, most of whom will require mechanical ventilation and intensive care [1,3]. However, currently there are few prognostic markers to forecast whether a patient with COVID-19 is likely to deteriorate to a critical condition and require intensive care. We thus suggest that data-driven risk evaluation based on machine learning approaches, such as artificial neural networks (ANNs) and decision trees, can and should play an important role in the prediction of morbidity and illness severity. Machine learning [4; 5; 6; 7; 8; 9] techniques are rapidly being employed for diverse medical applications [10], such as predicting diabetes [11] and liver diseases [12], detecting and predicting cancer [13], personalizing treatments [14], drug discovery [15], radiology [16], and many more. Such techniques employ statistical methods to identify complex relationships among patient medical attributes and outcomes, using large datasets. Two major medical domains that currently leverage machine learning usage are diagnosis and outcome prediction. In particular, machine learning can be a highly valuable tool for identifying individuals at high risk of health deterioration. Clinical reports of COVID-19 suggest that age, sex, and underlying comorbidities, such as renal diseases, hypertension, cardiovascular disease, and diabetes, can adversely affect patient outcomes [1]. To date, most machine learning applications for the COVID-19 pandemic have focused on patient diagnosis [17] and projecting the number of infected individuals and mortality rates [18]. Very few studies [19] have leveraged machine learning to systematically explore risk factors to assess the likely severity of disease in patients diagnosed with COVID-19 and to predict patient outcomes based on early clinical data. In this study, we introduce two types of machine learning models, a multilayer perceptron (MLP) artificial neural networks (ANN) [4] and Random Forest (RF) decision trees [20], to predict the level of severity of illness in patients diagnosed with COVID-19. The level of severity can be described as one of multiple clinical stages of a patient's hospital stay, such as admission to intensive care, the need for invasive mechanical ventilation, and in-hospital mortality. Our machine learning models were trained and validated using an open dataset provided by the Mexican Federal Health Secretary through the General Director of Epidemiology, see Supplementary Materials [21].