Journal of Machine Learning and Applications

ABSTRACT

The COVID-19 pandemic, originating in late 2019, has significantly affected worldwide health systems, economies, and everyday life. One of the major challenges faced by healthcare providers has been the varying severity of symptoms among individuals infected with the virus. While some patients experience mild or even asymptomatic cases, others suffer from severe symptoms that require intensive medical intervention. Early detection and accurate classification of symptom severity are crucial for effective treatment, resource allocation, and monitoring disease progression. In recent years, the integration of statistical methods and machine learning (ML) techniques has shown great promise in analyzing and classifying the severity of COVID-19 symptoms. These advanced approaches help health professionals identify high-risk patients, predict outcomes, and tailor treatment plans more effectively. In this article, we will explore how statistical techniques and machine learning models can be used to analyze COVID-19 symptom severity and discuss the benefits, challenges, and future directions of these approaches.

KEYWORDS

1. Articial neural networks
2. Big data
3. Clinical decision making
4. Decision support
5. Machine learning