Symptom Driven Predictive Models for Infectious Diseases, Primarily Leveraging Machine Learning Techniques

Abstract

An entirely new infectious illness, dubbed "Pandemic X," had a massive effect on worldwide affairs, influencing social, economic and political lives all over the planet. When the virus came back in April 2021 after the initial wave, the world was slowly coming to terms with the “new normal” and once again the consequences were devastating. Widespread testing was not possible due to limited resources and delays in the declaration of results. In this work, we introduce some machine learning predictive models that can predict the positive/negative status of patients using seven common symptoms recommended by the World Health Organization (WHO). Symptoms are Fever, Dry cough, Tiredness, Sore throat, Chest pain, Loss of taste/smell, and Breathing difficulties. Various criteria are used to make a comparative analysis of these models. Furthermore, we test our models with new data not included in our training and test sets in this study.