Prediction of COVID-19 Diagnosis based on Symptoms

Abstract

The COVID-19 pandemic has caused a significant global impact, creating a need for accurate prediction models. Such models can inform public health policies, guide resource allocation decisions, and assist individuals and society as a whole in decision-making. Several prediction models that combine multiple features to estimate infection risk have been developed using various data sources, such as case counts, testing rates, and demographic information. These models aim to assist healthcare professionals in triaging patients, especially in resource-limited settings. Our model accurately predicted COVID-19 test results using features such as sex, age 60 years, known contact with an infected person, and the presence of initial clinical symptoms. While no prediction model is perfect, they can provide valuable insights and contribute to the ongoing efforts to mitigate the impact of COVID-19.