Diagnosis, visualisation and analysis of COVID-19 using Machine learning - Anakal, Sudhir; K. Krishna Prasad; Chandrashekhar Uppin; M. Dileep Kumar (International Journal of Computational and Experimental Science and Engineering, 2025)
The Focal point of this paper is to point out or analyse the different kinds of symptoms and other complications COVID-19 Positive and Negative patients undergo. Coronaviruses are a club of viruses that attack humans with respiratory illness and their impact ranges from mild cold, fever, and dry cough to severe breathing problems, fatigue, chest pain and some other chronic problems. The objective of this research is to analyse the various chronic and other complications undergone by a COVID-19 patient. By considering most standard symptoms (given by WHO and Ministry of Health, govt of India), the data is collected from a renowned data repository called Kaggle and employed the best data analytical techniques to clean it so that it must befits our higher Machine Learning prediction aspirations. In this study, Ensemble machine learning models have been used, which take user input on some of the pre-defined approved standard symptoms and predict whether COVID-19 is present or not. The developed Machine Learning model cannot be left out like this, without any proper interface for duly picking up each data from the users, so we managed to reach out to a best and weighted framework termed Streamlit, for transforming our Machine Learning model into a fully-fledged and dual- faceted (Fill out the data manually by going into each cell or directly drop patient data in CSV file format) Web Application.