Analysis and Evaluation of Coronavirus In-Patient Prediction Model Algorithm in Machine Learning

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ID: 313174
2023
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Abstract
Coronavirus family belonged to the significant infection in both humans and animal’s pathogens. The current universal of the Coronavirus respiring ailment (COVID-19) shows a state of worldwide public outbreak. Coronavirus affliction (COVID-19) is spread through large droplets produced during coughing and sneezing by symptomatic patients, as well as asymptomatic individuals before beginning of their symptoms. The bug beginning the affliction is named harsh severe respiring condition coronavirus (SARS-CoV-2) and has happened flowing during the whole of the experience, producing dispassionate exhibitions grazing from asymptomatic cases to harsh severe respiring condition and obliteration, particularly of things accompanying comorbidities [hypertension, asthma, and diabetes]. Identify a suitable algorithm model that can predict either a patient has COVID-19, the model was trained on data from infected SARS-CoV-2 inmates. Machine Learning a model identify is projected to label best choice invention; to judge the traits that influence the forecast model. To find highest in rank treasure for the forecast model an orderly composition review is administered. The number of algorithms recognized from experiments and deep study holds SVM and Logistic Regression namely ideal for prophecy. When the result of the identify was reasoning refine was found that SVM Model acted better than the alternative invention. Two classifier, Support Vector Machine and Logistic Regression, were selected; both have an accuracy of more than 90%. The SVM has 96.49% and LR 95.92% accuracy. These results can be used to take corrective measure by different governmental hospitals bodies. The infrastructure of methodology for forecasting infections disease can make it easier to fight COVID-19.
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Authors Sadaf Hussain
Journal Journal of Computing & Biomedical Informatics
Year 2023
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/236
Keywords Keywords not found

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