A comparative study for determining Covid-19 risk levels by unsupervised machine learning methods

Fidan, Hüseyin; Yüksel, Mehmet Erkan

doi:10.1016/j.eswa.2021.116243

Cited by 10 publications

(7 citation statements)

References 35 publications

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“…Benito-León et al (2021) use unsupervised ML models to identify severity subclusters among patients infected with COVID-19 based on laboratory tests and clinical data. Fidan and Yuksel (2022) identify cities with similar risk levels of COVID-19 infection using unsupervised clustering algorithms. Khmaissia et al (2020) identify the key factors that influence the increased spread of the COVID-19 disease in New York City.…”

Section: Literature Reviewmentioning

confidence: 99%

Analyzing the research trends of COVID-19 using topic modeling approach

Trivedi

Patra²,

Singh³

et al. 2022

JM2

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Purpose The COVID-19 pandemic has impacted 222 countries across the globe, with millions of people losing their lives. The threat from the virus may be assessed from the fact that most countries across the world have been forced to order partial or complete shutdown of their economies for a period of time to contain the spread of the virus. The fallout of this action manifested in loss of livelihood, migration of the labor force and severe impact on mental health due to the long duration of confinement to homes or residences. Design/methodology/approach The current study identifies the focus areas of the research conducted on the COVID-19 pandemic. Abstracts of papers on the subject were collated from the SCOPUS database for the period December 2019 to June 2020. The collected sample data (after preprocessing) was analyzed using Topic Modeling with Latent Dirichlet Allocation. Findings Based on the research papers published within the mentioned timeframe, the study identifies the 10 most prominent topics that formed the area of interest for the COVID-19 pandemic research. Originality/value While similar studies exist, no other work has used topic modeling to comprehensively analyze the COVID-19 literature by considering diverse fields and domains.

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Section: Literature Reviewmentioning

confidence: 99%

Analyzing the research trends of COVID-19 using topic modeling approach

Trivedi

Patra²,

Singh³

et al. 2022

JM2

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“…Tu et al ( 29 ) designed the scoring system with expert consultation and calculated the import, spread, and combined risk scores of regions using quantitative analysis methods to determine the risk level. Using unsupervised machine learning techniques, Fidan et al ( 30 ) applied two clustering methods to classify COVID-19 risk degree.…”

Section: Introductionmentioning

confidence: 99%

Study on the regional risk classification method for the prevention and control of emerging infectious diseases based on directed graph theory

Liu,

Wang,

Zhang

2023

Front. Public Health

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BackgroundEmerging infectious diseases are a class of diseases that are spreading rapidly and are highly contagious. It seriously affects social stability and poses a significant threat to human health, requiring urgent measures to deal with them. Its outbreak will very easily lead to the large-scale spread of the virus, causing social problems such as work stoppages and traffic control, thereby causing social panic and psychological unrest, affecting human activities and social stability, and even endangering lives. It is essential to prevent and control the spread of infectious diseases effectively.PurposeWe aim to propose an effective method to classify the risk level of a new epidemic region by using graph theory and risk classification methods to provide a theoretical reference for the comprehensive evaluation and determination of epidemic prevention and control, as well as risk level classification.MethodsUsing the graph theory method, we first define the network structure of social groups and construct the risk transmission network of the new epidemic region. Then, combined with the risk classification method, the classification of high, medium, and low risk levels of the new epidemic region is discussed from two cases with common and looped graph nodes, respectively. Finally, the reasonableness of the classification method is verified by simulation data.ResultsThe directed weighted scale-free network can better describe the transmission law of an epidemic. Moreover, the proposed method of classifying the risk level of a region by using the correlation function between two regions and the risk value of the regional nodes can effectively evaluate the risk level of different regions in the new epidemic region. The experiments show that the number of medium and high risk nodes shows no increasing trend. The number of high-risk regions is relatively small compared to medium-risk regions, and the number of low-risk regions is the largest.ConclusionsIt is necessary to distinguish scientifically between the risk level of the epidemic area and the neighboring regions so that the constructed social network model of the epidemic region's spread risk can better describe the spread of the epidemic risk in the social network relations.

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“…It is the first algorithm to recall its input thanks to its internal memory, idealizing ML issues involving sequential data [65]. Besides, GANs are an interesting ML technique [66]. GANs are generative models, which means they produce new data instances that are similar to the training data [67].…”

Section: Introductionmentioning

confidence: 99%

Machine learning applications for COVID-19 outbreak management

Heidari

Navimipour

Ünal

et al. 2022

Neural Comput & Applic

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Recently, the COVID-19 epidemic has resulted in millions of deaths and has impacted practically every area of human life. Several machine learning (ML) approaches are employed in the medical field in many applications, including detecting and monitoring patients, notably in COVID-19 management. Different medical imaging systems, such as computed tomography (CT) and X-ray, offer ML an excellent platform for combating the pandemic. Because of this need, a significant quantity of study has been carried out; thus, in this work, we employed a systematic literature review (SLR) to cover all aspects of outcomes from related papers. Imaging methods, survival analysis, forecasting, economic and geographical issues, monitoring methods, medication development, and hybrid apps are the seven key uses of applications employed in the COVID-19 pandemic. Conventional neural networks (CNNs), long short-term memory networks (LSTM), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, random forest, and other ML techniques are frequently used in such scenarios. Next, cutting-edge applications related to ML techniques for pandemic medical issues are discussed. Various problems and challenges linked with ML applications for this pandemic were reviewed. It is expected that additional research will be conducted in the upcoming to limit the spread and catastrophe management. According to the data, most papers are evaluated mainly on characteristics such as flexibility and accuracy, while other factors such as safety are overlooked. Also, Keras was the most often used library in the research studied, accounting for 24.4 percent of the time. Furthermore, medical imaging systems are employed for diagnostic reasons in 20.4 percent of applications.

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A comparative study for determining Covid-19 risk levels by unsupervised machine learning methods

Cited by 10 publications

References 35 publications

Analyzing the research trends of COVID-19 using topic modeling approach

Analyzing the research trends of COVID-19 using topic modeling approach

Study on the regional risk classification method for the prevention and control of emerging infectious diseases based on directed graph theory

Machine learning applications for COVID-19 outbreak management

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