An Improved Machine Learnings Diagnosis Technique for COVID-19 Pandemic Using Chest X-ray Images

The pandemic produced by coronavirus2 (COVID-19) has confined the world, and avoiding close human contact is still suggested to combat the outbreak although the vaccination campaigns. It is expectable that emerging technologies have prominent roles to play during this pandemic, and the use of Artificial Intelligence (AI) has been proved useful in this direction. The use of AI by researchers in developing novel models for diagnosis, classification, and prediction of COVID-19 has really assist reduce the spread of the outbreak. Therefore, this paper proposes a machine learning diagnostic system to combat the spread of COVID-19. Four machine learning algorithms: Random Forest (RF), XGBoost, and Light Gradient Boosting Machine (LGBM) were used for quick and better identification of potential COVID-19 cases. The dataset used contains COVID-19 symptoms and selects the relevant symptoms of the diagnosis of a suspicious individual. The experiments yielded the LGBM leading with an accuracy of 0.97, recall of 0.96, precision of 0.97, F1-Score of 0.96, and ROC of 0.97 respectively. The real-time data capture would effectively diagnose and monitor COVID-19 patients, as revealed by the results.