Prediction of Corona-Virus Using Deep Learning

With the rapid spread of Corona virus in most parts worldwide, it has become necessary to find solutions contain and treat this epidemic. This research presents a method predict occurrence COVID-19 based on different symptoms disease, using non-clinical methods such as artificial intelligence, help medical staff, save cost testing (PCR), get results short time. Artificial intelligence provides many tools for data analysis, statistical intelligent research. In paper, we focus predicting infection, Neural Networks (ANN), random forests decision trees, effectively analyze datasets, common acute symptoms, cough, fever, headache, diarrhea, living infected areas Pain shortness breath. Breathing, chills, nasal congestion some other disease. A set consisting (1495) patients is used determine whether or not person after determining that appear it. The divided into 75% training 25% test applying deep learning algorithms. Python libraries pandas, NumPy, matplotlib are also addition sklearn Keras. search show very high accuracy indicated by 91% Random Forest with estimators = 200 tree. an neural network 85%. Thus, important indicator possible prediction infection.