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Application of machine learning for the diagnosis of COVID-19

ABSTRACT: This chapter focuses on the application of machine learning algorithms on the diagnosis of the novel coronavirus disease (COVID-19). First, data visualization is provided on increases in confirmed deaths and recovered cases of COVID-19 using currently available data from Johns Hopkins University. Next, the machine learning algorithms are used for the automatic diagnosis of COVID-19. Data-driven diagnosis is performed using a dataset of 5644 samples with 111 attributes provided by Hospital Israelita Albert Einstein, Brazil. As a preprocessing step, null values and categorical data are processed and standardization is performed. Next, feature selection is performed to find attributes that are most important for a COVID-19 diagnosis. A number of algorithms including random forest logistic regression, XGBoost, and decision tree are considered and their kernel parameters are optimized. The performance of classification algorithms is evaluated in terms of a number of factors including the testing accuracy, precision, recall, miss rate, receiver operating characteristic curve and area under the receiver operating characteristic curve. Experimental results show that serum glucose is the most influential attribute in predicting COVID-19. Our results also show that for the case of cross-validation, XGBoost has the highest accuracy value of 92.67% and logistic regressions have the second highest accuracy of 92.58%, whereas both XGBoost and LR have a 93% value for precision, recall, and F1 score. Moreover, for the case of the holdout method with 20% testing data, logistic regression with an accuracy of 94.06% outperforms other classifiers in terms of accuracy, precision, recall, and F1 score.

SUBMITTER: Podder P

PROVIDER: S-EPMC8137818 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAccurate and timely diagnosis and effective prognosis of the disease is important to provide the best possible care for patients with COVID-19 and reduce the burden on the health care system. Machine learning methods can play a vital role in the diagnosis of COVID-19 by processing chest x-ray images.ObjectiveThe aim of this study is to summarize information on the use of intelligent models for the diagnosis and prognosis of COVID-19 to help with early and timely diagnosis, minimize prolonged diagnosis, and improve overall health care.MethodsA systematic search of databases, including PubMed, Web of Science, IEEE, ProQuest, Scopus, bioRxiv, and medRxiv, was performed for COVID-19-related studies published up to May 24, 2020. This study was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. All original research articles describing the application of image processing for the prediction and diagnosis of COVID-19 were considered in the analysis. Two reviewers independently assessed the published papers to determine eligibility for inclusion in the analysis. Risk of bias was evaluated using the Prediction Model Risk of Bias Assessment Tool.ResultsOf the 629 articles retrieved, 44 articles were included. We identified 4 prognosis models for calculating prediction of disease severity and estimation of confinement time for individual patients, and 40 diagnostic models for detecting COVID-19 from normal or other pneumonias. Most included studies used deep learning methods based on convolutional neural networks, which have been widely used as a classification algorithm. The most frequently reported predictors of prognosis in patients with COVID-19 included age, computed tomography data, gender, comorbidities, symptoms, and laboratory findings. Deep convolutional neural networks obtained better results compared with non-neural network-based methods. Moreover, all of the models were found to be at high risk of bias due to the lack of information about the study population, intended groups, and inappropriate reporting.ConclusionsMachine learning models used for the diagnosis and prognosis of COVID-19 showed excellent discriminative performance. However, these models were at high risk of bias, because of various reasons such as inadequate information about study participants, randomization process, and the lack of external validation, which may have resulted in the optimistic reporting of these models. Hence, our findings do not recommend any of the current models to be used in practice for the diagnosis and prognosis of COVID-19.

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Application of machine learning for the diagnosis of COVID-19

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