Prediction of COVID-19 corona virus pandemic based on time series data using support vector machine

Singh, Vijander; Poonia, Ramesh Chandra; Kumar, Sandeep; Dass, Pranav; Agarwal, Pankaj; Bhatnagar, Vaibhav; Raja, Linesh

doi:10.1080/09720529.2020.1784535

Cited by 174 publications

(63 citation statements)

References 23 publications

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“…The disadvantage of the MFCC feature extraction method is that it does not provide the necessary accuracy in the analysis of noisy audio signals (Kohshelan 2014 ). In order to perform the classification process, the SVM classification method, which has gained momentum in this field was selected (Ismael and Şengür 2021 ; Singh et al 2020 ; Loey et al 2021 ). When the study is extended in terms of datasets, this classification technique should be reviewed for its use, because the SVM algorithm is sometimes not suited enough for large and noisy datasets.…”

Section: Discussionmentioning

confidence: 99%

Identifying COVID-19 by using spectral analysis of cough recordings: a distinctive classification study

Manshouri

2021

Cogn Neurodyn

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Sound signals from the respiratory system are largely taken as tokens of human health. Early diagnosis of respiratory tract diseases is of great importance because, if delayed, it exerts irreversible effects on human health. The Coronavirus pandemic, which is deeply shaking the world, has revealed the importance of this diagnosis even more. During the pandemic, it has become the focus of researchers to differentiate symptoms from similar diseases such as influenza. Among these symptoms, the difference in cough sound played a distinctive role in research. Clinical data collected under the supervision of doctors in a reliable environment were used as the dataset consisting of 16 subjects suspected of COVID-19 with a specific patient demographic. Using the polymerase chain reaction test, the suspected subjects were divided into two groups as negative and positive. The negative and positive labels represent the patients with non-COVID and with a COVID-19 cough, respectively. Using the 3D plot or waterfall representation of the signal frequency spectrum, the salient features of the cough data are revealed. In this way, COVID-19 can be differentiated from other coughs by applying effective feature extraction and classification techniques. Power spectral density based on short-time Fourier transform and mel-frequency cepstral coefficients (MFCC) were chosen as the efficient feature extraction method. From among the classification techniques, the support vector machine (SVM) algorithm was applied to the processed signals in order to identify and classify COVID-19 cough. In terms of results evaluation, the cough of subjects with COVID-19 was detected with 95.86% classification accuracy thanks to the radial basis function (RBF) kernel function of SVM and the MFCC method. The diagnosis of COVID-19 coughs was performed with 98.6% and 91.7% sensitivity and specificity, respectively.

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Section: Discussionmentioning

confidence: 99%

Identifying COVID-19 by using spectral analysis of cough recordings: a distinctive classification study

Manshouri

2021

Cogn Neurodyn

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“…It has been in numerous real-world applications, including the health sector, due to its high accuracy and performance. Therefore, recently, SVM has been used for combating the COVID-19 pandemic because of its superior performance ( Singh et al, 2020 , Ismael and Şengür, 2021 ). Various articles published on the detection ( Hassanien et al, 2020 , Yao et al, 2020 , Sethy et al, 2020 ), classification ( Barstugan et al, 2020 , Randhawa et al, 2020 ), and prediction and forecasting ( Hazarika and Gupta, 2020 , Ribeiro et al, 2020 , Pourhomayoun and Shakibi, 2020 , Fang et al, 2020 , Sun et al, 2020 , Ella Hassanien et al, 2020 , de Batista, 2020 ) have been discussed in this sub-section.…”

Section: Applications Of Ai To Combat Covid-19mentioning

confidence: 99%

Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review

Khan

Mehran

Haq

et al. 2021

Expert Systems with Applications

147

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During the current global public health emergency caused by novel coronavirus disease 19 (COVID-19), researchers and medical experts started working day and night to search for new technologies to mitigate the COVID-19 pandemic. Recent studies have shown that artificial intelligence (AI) has been successfully employed in the health sector for various healthcare procedures. This study comprehensively reviewed the research and development on state-of-the-art applications of artificial intelligence for combating the COVID-19 pandemic. In the process of literature retrieval, the relevant literature from citation databases including ScienceDirect, Google Scholar, and Preprints from arXiv, medRxiv, and bioRxiv was selected. Recent advances in the field of AI-based technologies are critically reviewed and summarized. Various challenges associated with the use of these technologies are highlighted and based on updated studies and critical analysis, research gaps and future recommendations are identified and discussed. The comparison between various machine learning (ML) and deep learning (DL) methods, the dominant AI-based technique, mostly used ML and DL methods for COVID-19 detection, diagnosis, screening, classification, drug repurposing, prediction, and forecasting, and insights about where the current research is heading are highlighted. Recent research and development in the field of artificial intelligence has greatly improved the COVID-19 screening, diagnostics, and prediction and results in better scale-up, timely response, most reliable, and efficient outcomes, and sometimes outperforms humans in certain healthcare tasks. This review article will help researchers, healthcare institutes and organizations, government officials, and policymakers with new insights into how AI can control the COVID-19 pandemic and drive more research and studies for mitigating the COVID-19 outbreak.

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“…In another study, Singh et al presented a support vector machine (SVM)-based prediction model to forecast the coronavirus cases for 10 countries having maximum number of coronavirus cases [ 18 ]. Yadav et al [ 38 ] also presented the spreading pattern of coronavirus in the top ten infected countries.…”

Section: Related Workmentioning

confidence: 99%

“… The present work also implemented two machine learning approaches SVM model, LR model and one deep learning approach CNN model to compare the performance in terms of accuracy and error rate. These three models are applied by authors in their work [ 18 – 20 ]. …”

Section: Introductionmentioning

confidence: 99%

Machine learning-based diffusion model for prediction of coronavirus-19 outbreak

Raheja

Kasturia

Cheng

et al. 2021

Neural Comput & Applic

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The coronavirus pandemic has been globally impacting the health and prosperity of people. A persistent increase in the number of positive cases has boost the stress among governments across the globe. There is a need of approach which gives more accurate predictions of outbreak. This paper presents a novel approach called diffusion prediction model for prediction of number of coronavirus cases in four countries: India, France, China and Nepal. Diffusion prediction model works on the diffusion process of the human contact. Model considers two forms of spread: when the spread takes time after infecting one person and when the spread is immediate after infecting one person. It makes the proposed model different over other state-of-the art models. It is giving more accurate results than other state-of-the art models. The proposed diffusion prediction model forecasts the number of new cases expected to occur in next 4 weeks. The model has predicted the number of confirmed cases, recovered cases, deaths and active cases. The model can facilitate government to be well prepared for any abrupt rise in this pandemic. The performance is evaluated in terms of accuracy and error rate and compared with the prediction results of support vector machine, logistic regression model and convolution neural network. The results prove the efficiency of the proposed model.

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Prediction of COVID-19 corona virus pandemic based on time series data using support vector machine

Cited by 174 publications

References 23 publications

Identifying COVID-19 by using spectral analysis of cough recordings: a distinctive classification study

Identifying COVID-19 by using spectral analysis of cough recordings: a distinctive classification study

Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review

Machine learning-based diffusion model for prediction of coronavirus-19 outbreak

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