Coronavirus disease (COVID-19) cases analysis using machine-learning applications (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.
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...
A Review of the Machine Learning Algorithms for Covid-19 Case Analysis
IEEE Transactions on Artificial Intelligence, 2022
The purpose of this article is to see how machine learning (ML) algorithms and applications are used in the COVID-19 inquiry and for other purposes. The available traditional methods for COVID-19 international epidemic prediction, researchers and authorities have given more attention to simple statistical and epidemiological methodologies. The inadequacy and absence of medical testing for diagnosing and identifying a solution is one of the key challenges in preventing the spread of COVID-19. A few statistical-based improvements are being strengthened to answer this challenge, resulting in a partial resolution up to a certain level. ML have advocated a wide range of intelligence-based approaches, frameworks, and equipment to cope with the issues of the medical industry. The application of inventive structure, such as ML and other in handling COVID-19 relevant outbreak difficulties, has been investigated in this article. The major goal of this article is to 1) Examining the impact of the data type and data nature, as well as obstacles in data processing for COVID-19. 2) Better grasp the importance of intelligent approaches like ML for the COVID-19 pandemic. 3) The development of improved ML algorithms and types of ML for COVID-19 prognosis. 4) Examining the effectiveness and influence of various strategies in COVID-19 pandemic. 5) To target on certain potential issues in COVID-19 diagnosis in order to motivate academics to innovate and expand their knowledge and research into additional COVID-19-affected industries. Impact Statement-The worldwide response to the COVID-19 epidemic will rely heavily on ML, defined broadly. This article enables ML researchers to quickly connect with the range of active research effort. We identify the key difficulties, potential paths for future work, and crucial community resources in particular. Given the multidisciplinary character of the problem, this review will aid data scientists in forming cross-disciplinary collaborations. We also educate strategists and policymakers on the advantages of ML and help them understand the obstacles, possibilities, and drawbacks of utilizing data science to battle the COVID-19 epidemic.
Machine Learning Applied to COVID-19: A Review of the Initial Pandemic Period
International Journal of Computational Intelligence Systems
Diagnostic and decision-making processes in the 2019 Coronavirus treatment have combined new standards using patient chest images, clinical and laboratory data. This work presents a systematic review aimed at studying the Artificial Intelligence (AI) approaches to the patients’ diagnosis or evolution with Coronavirus 2019. Five electronic databases were searched, from December 2019 to October 2020, considering the beginning of the pandemic when there was no vaccine influencing the exploration of Artificial Intelligence-based techniques. The first search collected 839 papers. Next, the abstracts were reviewed, and 138 remained after the inclusion/exclusion criteria was performed. After thorough reading and review by a second group of reviewers, 64 met the study objectives. These papers were carefully analyzed to identify the AI techniques used to interpret the images, clinical and laboratory data, considering a distribution regarding two variables: (i) diagnosis or outcome and (ii) t...
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.
Prediction of Covid-19 Outbreak Using Machine Learning
International Journal of Research Publication and Reviews
Coronavirus disease 2019 (COVID-19) is spreading rapidly; machine learning algorithms have been applied for a long time in many applications requiring the detection of adverse risk factors. The machine learning model proposed in this research paper uses three types of data, confirmed cases, recovered cases and deaths reported, the model can predict the spread of the virus in the next 20 days, and the data is time line series data and that is effective in predicting new cases of corona, death numbers and recovery.
A Machine Learning technique to analyze and detect Corona Virus
2022
COVID 19 has expanded repeatedly over the whole world, and the number of infected people has been increasing tremendously. COVID 19 has stormed the world in a blink resulting in millions of deaths with economic downfall around the globe. It has triggered a disastrous paradigm shift for the world. Given the unavoidable circumstances, testing for the virus on a rapid daily basis for million people yields the importance of partaking next steps in virus control. The supply chain of traditional Checkup and report time is exorbitant and has the avenue of exceeding the possibility of misreporting. As a result, we have presented Machine learning-based methods for COVID-19 identification. To improve the COVID 19 prediction algorithm, this study indicates the use of exhaustive profiling, SMOTE (Synthetic Minority Oversampling Technique), a classification model, and a deep learning model. This paper goals to provide Machine learning classifier algorithms and Neural Networks with selected attributes to obtain better accuracy and efficacy with a subsequent comparison with different algorithms.
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.
Electronics
Since November 2019, the COVID-19 Pandemic produced by Severe Acute Respiratory Syndrome Severe Coronavirus 2 (hereafter COVID-19) has caused approximately seven million deaths globally. Several studies have been conducted using technological tools to prevent infection, to prevent spread, to detect, to vaccinate, and to treat patients with COVID-19. This work focuses on identifying and analyzing machine learning (ML) algorithms used for detection (prediction and diagnosis), monitoring (treatment, hospitalization), and control (vaccination, medical prescription) of COVID-19 and its variants. This study is based on PRISMA methodology and combined bibliometric analysis through VOSviewer with a sample of 925 articles between 2019 and 2022 derived in the prioritization of 32 papers for analysis. Finally, this paper discusses the study’s findings, which are directions for applying ML to address COVID-19 and its variants.