<p>In 2019 appeared a new Coronavirus Disease (COVID-19) in China, spreading rapidly globally and causing a pandemic with high infection and death numbers. To prevent a collapse of the health institutions, accurate decision making about assignments of intense care units (ICU) is required, depending on the probable outcome. The usage of machine learning (ML) for other medical fields had been successful before. So we applied ML techniques to a dataset of COVID-19 and influenza patients from Mexico to predict the severity of an individual’s infection regarding risk factors including, but not limited to, chronic obstructive pulmonary disease (COPD), cardiovascular disease, diabetes, asthma, immunosupression, and obesity. We conducted two experiments, one on hospitalised patients and the other one on a balanced dataset. The resulting applications should not be used as a diagnostic tool yet, due to a relatively short time period of data collection and 74.64% accuracy for the first experiment and 82.61% accuracy for the second one. Nonetheless it is a good starting point to continue research about predicting COVID-19 infection’s outcome based on risk factors.</p>

<p>In 2019 appeared a new Coronavirus Disease (COVID-19) in China, spreading rapidly globally and causing a pandemic with high infection and death numbers. To prevent a collapse of the health institutions, accurate decision making about assignments of intense care units (ICU) is required, depending on the probable outcome. The usage of machine learning (ML) for other medical fields had been successful before. So we applied ML techniques to a dataset of COVID-19 and influenza patients from Mexico to predict the severity of an individual’s infection regarding risk factors including, but not limited to, chronic obstructive pulmonary disease (COPD), cardiovascular disease, diabetes, asthma, immunosupression, and obesity. We conducted two experiments, one on hospitalised patients and the other one on a balanced dataset. The resulting applications should not be used as a diagnostic tool yet, due to a relatively short time period of data collection and 74.64% accuracy for the first experiment and 82.61% accuracy for the second one. Nonetheless it is a good starting point to continue research about predicting COVID-19 infection’s outcome based on risk factors.</p>