Using Machine Learning to assess Covid-19 risks (original) (raw)
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International Journal of Engineering Research & Technology, 2020
In the current situation due to the similar symptoms of both covid-19 and flu many people are unaware between covid-19 and flu which may lead to demise of a person. So sort of methods are required to classify the symptoms between covid-19 and other disease to control the demise rate. Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. Most people infected with the COVID-19 virus will experience mild to moderate respiratory illness and recover without requiring special treatment. Older people, and those with underlying medical problems like cardiovascular disease, diabetes, chronic respiratory disease, and cancer are more likely to develop serious illness. In the lighting view of current pandemic situation diagnosis of these disease is done only through some clinical tests like RT-PCR, CT-Scan of lung images to identify the covid-19 since these tests take both much time and also very expensive, we will be implementing a solution to overcome these current problems faced by people in the pandemic situation. After taking a literature survey we got that image processing , data mining, machine learning, pattern classification are hig hly used methods to get solution of these problem.
Machine Learning-Based COVID-19 Diagnosis by Demographic Characteristics and Clinical Data
Advances in Respiratory Medicine
Introduction: To facilitate rapid and effective diagnosis of COVID-19, effective screening can alleviate the challenges facing healthcare systems. We aimed to develop a machine learning-based prediction of COVID-19 diagnosis and design a graphical user interface (GUI) to diagnose COVID-19 cases by recording their symptoms and demographic features. Methods: We imple-mented different classification models including support vector machine (SVM), Decision tree (DT), Naïve Bayes (NB) and K-nearest neighbor (KNN) to predict the result of COVID-19 test for individ-uals. We trained these models by data of 16973 individuals (90% of all individuals included in data gathering) and tested by 1885 individuals (10% of all individuals). Maximum relevance minimum redundancy (MRMR) algorithms used to score features for prediction of result of COVID-19 test. A user-friendly GUI was designed to predict COVID-19 test results in individuals. Results: Study re-sults revealed that coughing had the highest...
Machine learning based predictors for COVID-19 disease severity
Scientific Reports, 2021
Predictors of the need for intensive care and mechanical ventilation can help healthcare systems in planning for surge capacity for COVID-19. We used socio-demographic data, clinical data, and blood panel profile data at the time of initial presentation to develop machine learning algorithms for predicting the need for intensive care and mechanical ventilation. Among the algorithms considered, the Random Forest classifier performed the best with \text {AUC} = 0.80AUC=0.80forpredictingICUneedandAUC = 0.80 for predicting ICU need andAUC=0.80forpredictingICUneedand\text {AUC} = 0.82$$ AUC = 0.82 for predicting the need for mechanical ventilation. We also determined the most influential features in making this prediction, and concluded that all three categories of data are important. We determined the relative importance of blood panel profile data and noted that the AUC dropped by 0.12 units when this data was not included, thus indicating that it provided valuable information in predicting disease severity. Finally, we generated RF predi...
Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: A review
Informatics in Medicine Unlocked, 2021
The existence of widespread COVID-19 infections has prompted worldwide efforts to control and manage the virus, and hopefully curb it completely. One important line of research is the use of machine learning (ML) to understand and fight COVID-19. This is currently an active research field. Although there are already many surveys in the literature, there is a need to keep up with the rapidly growing number of publications on COVID-19-related applications of ML. This paper presents a review of recent reports on ML algorithms used in relation to COVID-19. We focus on the potential of ML for two main applications: diagnosis of COVID-19 and prediction of mortality risk and severity, using readily available clinical and laboratory data. Aspects related to algorithm types, training data sets, and feature selection are discussed. As we cover work published between January 2020 and January 2021, a few key points have come to light. The bulk of the machine learning algorithms used in these two applications are supervised learning algorithms. The established models are yet to be used in realworld implementations, and much of the associated research is experimental. The diagnostic and prognostic features discovered by ML models are consistent with results presented in the medical literature. A limitation of the existing applications is the use of imbalanced data sets that are prone to selection bias.
Frontiers in Medicine
BackgroundAt the end of 2019, the coronavirus disease 2019 (COVID-19) pandemic increased the hospital burden of COVID-19 caused by the SARS-Cov-2 and became the most significant health challenge for nations worldwide. The severity and high mortality of COVID-19 have been correlated with various demographic characteristics and clinical manifestations. Prediction of mortality rate, identification of risk factors, and classification of patients played a crucial role in managing COVID-19 patients. Our purpose was to develop machine learning (ML)-based models for the prediction of mortality and severity among patients with COVID-19. Identifying the most important predictors and unraveling their relationships by classification of patients to the low-, moderate- and high-risk groups might guide prioritizing treatment decisions and a better understanding of interactions between factors. A detailed evaluation of patient data is believed to be important since COVID-19 resurgence is underway i...
Individual Factors Associated With COVID-19 Infection: A Machine Learning Study
Frontiers in Public Health
The fast, exponential increase of COVID-19 infections and their catastrophic effects on patients' health have required the development of tools that support health systems in the quick and efficient diagnosis and prognosis of this disease. In this context, the present study aims to identify the potential factors associated with COVID-19 infections, applying machine learning techniques, particularly random forest, chi-squared, xgboost, and rpart for feature selection; ROSE and SMOTE were used as resampling methods due to the existence of class imbalance. Similarly, machine and deep learning algorithms such as support vector machines, C4.5, random forest, rpart, and deep neural networks were explored during the train/test phase to select the best prediction model. The dataset used in this study contains clinical data, anthropometric measurements, and other health parameters related to smoking habits, alcohol consumption, quality of sleep, physical activity, and health status durin...
Analyzing Mortality Risk of COVID-19 Patients Using Machine Learning
Zenodo (CERN European Organization for Nuclear Research), 2022
The abrupt increase in the number of illnesses and high fatality rates during the covid-19 epidemic have put enormous strain on public health services. As a result, identifying the main parameters for mortality prediction is essential for improving patient treatment plans. Early detection of patient mortality issues can help to avert death by ensuring optimal resource allocation and treatment planning. The primary goal of this research is to swiftly identify the Severe Covid-19 patient by looking at demographics, comorbidities, admission, laboratory data, admission medicines, admission oxygen therapy prescriptions, discharge, and mortality data. Various machine learning models (linear regression, decision trees, and KNN) were trained and their performance compared to determine the model that consistently achieves high accuracy during the disease's days.
Early risk assessment for COVID-19 patients from emergency department data using machine learning
Background Since its emergence in late 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic, with more than 4.8 million reported cases and 310 000 deaths worldwide. While epidemiological and clinical characteristics of COVID-19 have been reported, risk factors underlying the transition from mild to severe disease among patients remain poorly understood. Methods In this retrospective study, we analysed data of 820 confirmed COVID-19 positive patients admitted to a two-site NHS Trust hospital in London, England, between January 1st and April 23rd, 2020, with a majority of cases occurring in March and April. We extracted anonymised demographic data, physiological clinical variables and laboratory results from electronic healthcare records (EHR) and applied multivariate logistic regression, random forest and extreme gradient boosted trees. To evaluate the potential for early risk assessment, we used data available during patients' initial pres...
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.
The use and significance of machine learning to screening COVID-19 pandemic
IAES International Journal of Robotics and Automation (IJRA)
The 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 (ML) algorithms are largely applied in other areas, at this time every health care unit has to want to use ML techniques to find and predict, tracking, screening, spread COVID-19, and try to find the treatment of it. we show what is the journey of ML to find and track the COVID-19 virus and also observing it from a screening and detecting the COVID-19 virus. We show how much research has been done yet to dete...