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