Abstract
In the fourth quarter of the year 2019, the planet became overwhelmed by the pandemic caused by the coronavirus disease (COVID-19). This virus imperiled human life and have affected a considerable percentage of the world population much before its early stage detection mechanisms were discovered and made available at the grassroots level. As there is no specific drug available to treat this infection, the vaccine was intended to serve as the ultimate weapon in the war against this species of coronavirus, but like other viruses, being an RNA virus, this virus also mutates continuously while it passes from one human to the other, making the development of highly potent vaccines even more challenging. This work is being sketched at the juncture when a huge percentage of the human population is already affected by this virus globally. In this work, we are proposing an idea to develop an app to detect coronavirus (COVID-19) symptoms at an early stage by self-diagnosis at home or at the clinical level. An experimental study has been performed on a dummy dataset with 11000 entries of various breadth patterns based on the spirometry analysis, lung volume analysis, and lung capacity analysis of normal male subjects and detailed breath patterns of infected male patients. A logistic regression model is trained after using SMOTE oversampling to balance the data and the predictive accuracy levels of 80%, 78%, and 90%. The results accomplished through this study and experiments may not only aid the clinicians in their medical practice but may also bestow a blue chip to the masterminds engaged in the biomedical research for inventing more evolved, sophisticated, user-friendly, miniaturized, portable, and economical medical app/devices in the future.