Symptoms Based Covid-19 Disease Diagnosis Using Machine Learning Approach

Abstract

The catastrophic outbreak of SARS-CoV-2 or COVID-19 has taken the world to uncharted waters. Detecting such an outbreak at its early stages is crucial to minimize its spread but is very difficult as well. The pandemic situation is not yet under control as the virus tends to evolve and develop mutations. This further complicates the development of machine learning or AI models that can automatically detect the disease in the general public. However, researchers worldwide have been putting their incredible efforts into devising mechanisms that help analyze and control the pandemic situation. Many prediction models have been developed to predict COVID-19 infection risk that helps in mitigating the burden on the healthcare system. These models help the medical staff, especially when healthcare resources are limited. As a contribution to society’s well-being, this research work deploys a machine learning prediction model that predicts COVID-19 patients with COVID-19 symptoms. Key pieces of information from RT-PCR test data results by the Israeli ministry of health publicly available have been distilled, preprocessed, and then used to train our prediction model. The model is trained on eight features, out of which five are the primary clinical symptoms of this fatal virus: cough, sore throat, fever, headache, breath shortness; and the other three features are gender, test indication, and age. Machine learning models can be considered for COVID-19 testing, especially when resources are limited. We have achieved highly accurate results in COVID-19 prediction with our prediction model. The model is best suited in urgent situations where there is a limitation of testing resources.