Prediction of Corona-Virus Using Deep Learning
DOI:
https://doi.org/10.25130/tjps.v27i1.89Abstract
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.
Downloads
Published
How to Cite
License
Copyright (c) 2022 Tikrit Journal of Pure Science
This work is licensed under a Creative Commons Attribution 4.0 International License.
Tikrit Journal of Pure Science is licensed under the Creative Commons Attribution 4.0 International License, which allows users to copy, create extracts, abstracts, and new works from the article, alter and revise the article, and make commercial use of the article (including reuse and/or resale of the article by commercial entities), provided the user gives appropriate credit (with a link to the formal publication through the relevant DOI), provides a link to the license, indicates if changes were made, and the licensor is not represented as endorsing the use made of the work. The authors hold the copyright for their published work on the Tikrit J. Pure Sci. website, while Tikrit J. Pure Sci. is responsible for appreciate citation of their work, which is released under CC-BY-4.0, enabling the unrestricted use, distribution, and reproduction of an article in any medium, provided that the original work is properly cited.