A Machine Learning technique to analyze and detect Corona Virus (original) (raw)
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Machine Learning Approaches for Tackling Novel Coronavirus (COVID-19) Pandemic
SN Computer Science
Novel coronavirus (COVID-19) has become a global problem in recent times due to the rapid spread of this disease. Almost all the countries of the world have been affected by this pandemic that made a major consequence on the medical system and healthcare facilities. The healthcare system is going through a critical time because of the COVID-19 pandemic. Modern technologies such as deep learning, machine learning, and data science are contributing to fight COVID-19. The paper aims to highlight the role of machine learning approaches in this pandemic situation. We searched for the latest literature regarding machine learning approaches for COVID-19 from various sources like IEEE Xplore, PubMed, Google Scholar, Research Gate, and Scopus. Then, we analyzed this literature and described them throughout the study. In this study, we noticed four different applications of machine learning methods to combat COVID-19. These applications are trying to contribute in various aspects like helping physicians to make confident decisions, policymakers to take fruitful decisions, and identifying potentially infected people. The major challenges of existing systems with possible future trends are outlined in this paper. The researchers are coming with various technologies using machine learning techniques to face the COVID-19 pandemic. These techniques are serving the healthcare system in a great deal. We recommend that machine learning can be a useful tool for proper analyzing, screening, tracking, forecasting, and predicting the characteristics and trends of COVID-19.
SN Computer Science
As the number of COVID-19 cases increases day by day, the situation and livelihood of people throughout the world deteriorates. The goal of this study is to use machine learning models to identify disease and forecast whether or not a person is infected with the virus or another common illness. More articles about COVID-19 will be released starting in 2020, but we still do not have a reliable prediction mechanism to diagnose the disease with 100% accuracy. This comparison is done to see which model is the most effective in detecting and predicting disease. Despite the fact that we have immunizations, we require a best-prediction strategy to assist all humans in surviving. Researchers claimed that the supervised learning method predicts more accurately than the unsupervised learning method in the majority of studies. Supervised learning is the process of mapping inputs to derived outputs using a set of variables and created functions. This will also help us to optimize performance criteria using experience. It is further divided into two categories: classification and regression. According to recent studies, classification models are more accurate than other models.
Coronavirus disease (COVID-19) cases analysis using machine-learning applications
Applied Nanoscience
Today world thinks about coronavirus disease that which means all even this pandemic disease is not unique. The purpose of this study is to detect the role of machine-learning applications and algorithms in investigating and various purposes that deals with COVID-19. Review of the studies that had been published during 2020 and were related to this topic by seeking in Science Direct, Springer, Hindawi, and MDPI using COVID-19, machine learning, supervised learning, and unsupervised learning as keywords. The total articles obtained were 16,306 overall but after limitation; only 14 researches of these articles were included in this study. Our findings show that machine learning can produce an important role in COVID-19 investigations, prediction, and discrimination. In conclusion, machine learning can be involved in the health provider programs and plans to assess and triage the COVID-19 cases. Supervised learning showed better results than other Unsupervised learning algorithms by having 92.9% testing accuracy. In the future recurrent supervised learning can be utilized for superior accuracy.
Role of Machine Learning Techniques in COVID-19 Prediction and Detection
IRJET, 2022
Currently, the detection of coronavirus disease (COVID-19) is one of the main challenges in the world, given the rapid spread of the disease. Recent statistics indicate that the number of people diagnosed with COVID-19 pandemic is increasing exponentially, with more than 1.6 million confirmed cases. The disease is spreading to many countries across the world. In this study, we analyses the incidence of COVID-19 distribution across the world. Machine learning is an innovative approach that has extensive applications in prediction. This technique needs to be applied for the COVID-19 pandemic to identify patients at high risk, their death rate, and other abnormalities. It can be used to understand the nature of this virus and further predict the upcoming issues.
BioMedInformatics
The COVID-19 virus has made a huge impact on people’s lives ever since the outbreak happened in December 2019. Unfortunately, the COVID-19 virus has not completely vanished from the world yet, and thus, global agitation is still increasing with mutations and variants of the same. Early diagnosis is the best way to decline the mortality risk associated with it. This urges the necessity of developing new computational approaches that can analyze a large dataset and predict the disease in time. Currently, automated virus diagnosis is a major area of research for accurate and timely predictions. Artificial intelligent (AI)-based techniques such as machine learning (ML) and deep learning (DL) can be deployed for this purpose. In this, compared to traditional machine learning techniques, deep Learning approaches show prominent results. Yet it still requires optimization in terms of complex space problems. To address this issue, the proposed method combines deep learning predictive models ...
Machine Learning based Model to combat Covid 19
2020
As declared by World Health Organization (WHO) more than nine lakh confirmed cases and more than forty six thousand death worldwide occurred due to novel coronavirus-COVID 19 from December 1, 2019 to April 1, 2020. The origin of this virus was Wuhan, China. COVID-19 has now spread all over the world and declared as pandemic disease by World Health Organization. In this paper, a dataset of 119 patients is prepared and different machine learning classification algorithm like linear classifier, K-neighbor classifier, Support Vector Machine, Decision Tree, Boosted Tree, Random Forest and Neural Network has applied to find the most suitable method that can predict the possibility of coronavirus infection. After the survey of all the algorithms it was found that Extra Tree Classifier gives best result, which is used further to predict the status of the patient. Extra Tree Classifier gives 94% accuracy. User will give input and the algorithm will predict if the patient is infected by coron...
Analysis of Covid19 Disease using Machine Learning
International Journal of Advanced Scientific Innovation, 2021
COVID-19 outbreaks only affect the lives of people, they result in a negative impact on the economy of the country. On Jan. 30, 2020, it was declared as a health emergency for the entire globe by the World Health Organization (WHO). By Apr. 28, 2020, more than 3 million people were infected by this virus and there was no vaccine to prevent. The WHO released certain guidelines for safety, but they were only precautionary measures. The use of information technology with a focus on fields such as data Science and machine learning can help in the fight against this pandemic. It is important to have early warning methods through which one can forecast how much the disease will affect society, on the basis of which the government can take necessary actions without affecting its economy. A deep CNN architecture has been proposed in this paper for the diagnosis of COVID-19 based on the chest X-ray image classification. Due to the nonavailability of sufficient-size and good-quality chest X-ray image dataset, an effective and accurate CNN classification was a challenge. To deal with these complexities such as the availability of a very-smallsized and imbalanced dataset with image-quality issues, the dataset has been preprocessed in different phases using different techniques to achieve an effective training dataset for the proposed CNN model to attain its best performance. preprocessing stages of the datasets performed in this study include dataset balancing, medical experts' image analysis, and data augmentation. experimental results have shown the overall accuracy as high as 99.5% which demonstrates the good capability of the proposed CNN model in the current application domain.
Master s Final Project Machine Learning applied to COVID-19
2020
This work is focused on the impact of machine learning, on the COVID-19 pandemic. Machine learning has proven to be invaluable in predicting risks in many spheres and since the spread of the virus started, its application is helping us against the viral pandemic. Like never before, people all around the world are collecting and sharing what they learn about the virus. Hundreds of research teams are combining their efforts to collect data and develop solutions every day. Starting from this, the main goals of this work are: to shine a light on their work; going deep into how the application of machine learning techniques on different fields affected by the pandemic is helping us in the fight against the coronavirus; to identify strengths and weaknesses of machine learning techniques and the challenges for further progress in medical machine learning systems. This final master thesis report addresses recent studies that apply machine learning on multiple angles: screening and diagnosis...
Review Paper on Machine learning-based prediction of Corona Virus
IJRASET, 2021
Effective contact tracing of SARS-CoV-2 enables quick and efficient diagnosis of COVID-19 and might mitigate the burden on healthcare system. Prediction models that combine several features to approximate the danger of infection are developed. These aim to help medical examiners worldwide in treatment of patients, especially within the context of limited healthcare resources. They established a machine learning approach that trained on records from 51,831 tested individuals (of whom 4769 were confirmed to own COVID-19 coronavirus). Test set contained data from the upcoming week (47,401 tested individuals of whom 3624 were confirmed to own COVID-19 disease). Their model predicted COVID-19 test results with highest accuracy using only eight binary features: sex, age ≥60 years, known contact with infected patients, and also the appearance of 5 initial clinical symptoms appeared. Generally, supported the nationwide data publicly reported by the Israeli Ministry of Health, they developed a model that detects COVID-19 cases by simple features accessed by asking basic inquiries to the affected patient. Their framework may be used, among other considerations, to prioritize testing for COVID-19 when testing resources are limited and important.
The use and significance of machine learning to screen COVID-19
IAES International Journal of Robotics and Automation (IJRA), 2022
Coronavirus disease 2019 (COVID-19) virus was first seen in 2019 December in China and rapidly spread all over the world and millions of people are infected with this virus. This disease has sited the entire world in dangerous circumstances. At the start of this virus, it was a very serious matter in China but now it is being observed all over the world. The virus is life-threatening, and other public who are affected by previous diseases or those people whose age is more than 60 are more affected by this virus. The healthcare and drug industries have tried to find a treatment. While machine learning algorithms are largely applied in other areas, at this time every health care unit has to want to use machine learning techniques to find, predict, track, and screen the spread of COVID-19, and try to find the treatment of it. we show what is the journey of machine learning to find and track COVID-19 and also observing it from a screening and detecting the COVID-19. We show how much research has been done yet to detection of COVID-19 and which algorithm of machine learning is best for the detection and screening of the COVID-19.