The Novel Meta-Heuristic Optimization-Based Deep Learning Framework for Predicting Diagnoses with COVID-19 in Patients Undergoing Emergency Treatment

Abstract

The global medical system has faced enormous challenges as a result of the COVID-19 epidemic. Since emergency facilities are frequently the first places individuals with COVID-19 indications go, they are often in the center of the matter. Finding patients with COVID-19 and treating them appropriately while applying precautions to stop the virus from spreading among other individuals and medical personnel is one of the primary challenges EDs face. This work develops a Covid-19 forecasting system using deep learning via four essential steps. The incoming initial information is initially placed via the pre-processing phase to improve the information accuracy and efficacy evaluation of the suggested model. Data cleansing and normalization are done during the pre-processing phase. The best characteristics are chosen using meta-heuristic-based Belief Net Particle Swarm optimization (MH-Belief Net + PSO). Next, the covid-19 forecasting step is replicated using the newly improved Deep Learning (DL) approach, the optimizing deep belief network (DBN). The parameter modification enhances the system's capacity to forecast disease. An improved DBN's output shows if COVID-19 is present or nonexistent. Because of this, the effectiveness assessment significance of the suggested approaches was greater compared to each of existing approaches, including SVM, RF, CNN, and NB.