A Machine Learning Approach as an Aid for Early COVID-19 Detection

Velazquez, Roberto; Tobon, P V Diana; Sánchez, Agustín Rueda; Saddik, Abdulmotaleb El; Petriu, Emil M.

doi:10.3390/s21124202

Cited by 19 publications

(15 citation statements)

References 29 publications

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“…However, the accuracy obtained for the Mexican dataset was only 69%. Martinez-Velaquez et al [52] used ML for early detection of COVID-19 where 22 features were considered and a maximum sensitivity of 75% was obtained. Rezapour and Colin [53] used ML to understand the relationship between COVID-19 susceptibility and comorbidities.…”

Section: Further Discussionmentioning

confidence: 99%

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Supervised Learning Models for the Preliminary Detection of COVID-19 in Patients Using Demographic and Epidemiological Parameters

et al. 2022

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The World Health Organization labelled the new COVID-19 breakout a public health crisis of worldwide concern on 30 January 2020, and it was named the new global pandemic in March 2020. It has had catastrophic consequences on the world economy and well-being of people and has put a tremendous strain on already-scarce healthcare systems globally, particularly in underdeveloped countries. Over 11 billion vaccine doses have already been administered worldwide, and the benefits of these vaccinations will take some time to appear. Today, the only practical approach to diagnosing COVID-19 is through the RT-PCR and RAT tests, which have sometimes been known to give unreliable results. Timely diagnosis and implementation of precautionary measures will likely improve the survival outcome and decrease the fatality rates. In this study, we propose an innovative way to predict COVID-19 with the help of alternative non-clinical methods such as supervised machine learning models to identify the patients at risk based on their characteristic parameters and underlying comorbidities. Medical records of patients from Mexico admitted between 23 January 2020 and 26 March 2022, were chosen for this purpose. Among several supervised machine learning approaches tested, the XGBoost model achieved the best results with an accuracy of 92%. It is an easy, non-invasive, inexpensive, instant and accurate way of forecasting those at risk of contracting the virus. However, it is pretty early to deduce that this method can be used as an alternative in the clinical diagnosis of coronavirus cases.

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Section: Further Discussionmentioning

confidence: 99%

“…AI was used in early COVID-19 detection in [52]. Decision tree, Support Vector Machine and voting classifiers were used on the benchmarked dataset from Mexico.…”

Section: Related Workmentioning

confidence: 99%

Supervised Learning Models for the Preliminary Detection of COVID-19 in Patients Using Demographic and Epidemiological Parameters

et al. 2022

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“…Shams et al [12], using 5 symptom-based features, obtained an accuracy of 77.59%, with precision and recall being 74% and 84%, respectively. Martinez-Velazquez et al [13] obtained an accuracy of 68.4% from 14 symptom-based features.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this study, two separate datasets were used. We predicted the COVID-19 infection possibility using the dataset available at Kaggle [22], and the mortality risk was forecasted using the Martinez-Velazquez et al dataset [13].…”

Section: Datasetmentioning

confidence: 99%

“…This inspired many research works in diagnosing the COVID-19 infected patients using the symptoms [10][11][12]. Moreover, this idea extended into predicting the mortality risk of critically infected patients from their symptoms [13,14]. This can significantly reduce the number of deaths from the infection by providing the necessary healthcare beforehand.…”

Section: Introductionmentioning

confidence: 99%

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COVID-19 Prognosis and Mortality Risk Predictions from Symptoms: A Cloud-Based Smartphone Application

Monjur

Preo

Shams

et al. 2021

BioMed

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The coronavirus pandemic overwhelmed many countries and their healthcare systems. Shortage of testing kits and Intensive-Care-Unit (ICU) beds for critical patients have become a norm in most developing countries. This has prompted the need to rapidly identify the COVID-19 patients to stop the spread of the virus and also to find critical patients. The latter is imperative for determining the state of critically ill patients as quickly as possible. This will lower the number of deaths from the infection. In this paper, we propose a cloud-based smartphone application for the early prognosis of COVID-19 infected patients and also predict their mortality risk using their symptoms. Moreover, we heuristically identified the most important symptoms necessary for making such predictions. We have successfully reduced the number of features by almost half for the prognosis and by more than a third for forecasting the mortality risk, compared to the contemporary studies. The application makes the real-time analysis using machine learning models, designed and stored in the cloud. Our machine learning model demonstrates an accuracy, precision, recall, and F1 score of 97.72%, 100%, 95.55%, and 97.70%, respectively, in identifying the COVID-19 infected patients and with an accuracy, precision, recall, and F1 score of 90.83%, 88.47%, 92.94%, and 90.65%, respectively, in forecasting the mortality risk from the infection. The real-time cloud-based approach yields faster responses, which is critical in the time of pandemic for mitigating the infection spread and aiding in the efficient management of the limited ICU resources.

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Prediction Models for COVID-19 in Children

Magboo

2022

Innovation in Medicine and Healthcare

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A Machine Learning Approach as an Aid for Early COVID-19 Detection

Cited by 19 publications

References 29 publications

Supervised Learning Models for the Preliminary Detection of COVID-19 in Patients Using Demographic and Epidemiological Parameters

Supervised Learning Models for the Preliminary Detection of COVID-19 in Patients Using Demographic and Epidemiological Parameters

COVID-19 Prognosis and Mortality Risk Predictions from Symptoms: A Cloud-Based Smartphone Application

Prediction Models for COVID-19 in Children

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