Application of Machine Learning on Predicting the Risk of Death of Respiratory Infectious Diseases Patients Using COVID-19 as Example

In modern society, with the accelerating pace of urbanization and the increasing frequency of global travel, densely populated areas have become hotspots for the outbreak of infectious diseases. These areas, such as large-scale urban centers and crowded public transportation hubs, facilitate the rapid spread of pathogens. The patients affected by infectious diseases span a wide spectrum of different groups, including the elderly with weakened immune systems, children whose immune systems are still developing, and individuals with pre-existing chronic health conditions. The impact of infectious diseases on patients varies significantly. Depending on a patient's underlying health condition, the course of the disease can range from a mild, self-limiting illness to a severe, life-threatening condition. For example, in the case of influenza, some healthy individuals may experience only mild symptoms like a runny nose and mild fever, while the elderly or those with respiratory diseases may develop severe pneumonia, which can be fatal. In the context of the ongoing global health challenges, such as the COVID - 19 pandemic, there is an urgent need to explore innovative methods for predicting the risk of patients infected by infectious diseases. To demonstrate whether machine learning techniques can be effectively used for this purpose, this study employs neural networks to develop a model specifically designed to predict the risk of death among COVID-19 patients. Through rigorous testing and validation, the model has been proven to have a high degree of accuracy in predicting these risks. This indicates that the method proposed in this study can be applied to identify which patients have a more urgent need for medical resources, thus providing valuable reference for healthcare providers to optimize the allocation of healthcare resources.