About: A Classification Approach for Predicting COVID-19 Patient Survival Outcome with Machine Learning Techniques

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	A Classification Approach for Predicting COVID-19 Patient Survival Outcome with Machine Learning Techniques
Creator	Abdu, Mannir Ado Osi, Abdulhameed Dikko, Garba Hussaini, § Ibrahim, Auwalu Isma'il, Adamu Lawan, § Muhammad, Usman Ringim, Muftahu Sada, Sani Safiya, § Sarki Abdulkadir, Hasssan Suleiman, Ahmad
source	MedRxiv
abstract	COVID-19 is an infectious disease discovered after the outbreak began in Wuhan, China, in December 2019. COVID-19 is still becoming an increasing global threat to public health. The virus has been escalated to many countries across the globe. This paper analyzed and compared the performance of three different supervised machine learning techniques; Linear Discriminant Analysis (LDA), Random Forest (RF), and Support Vector Machine (SVM) on COVID-19 dataset. The best level of accuracy between these three algorithms was determined by comparison of some metrics for assessing predictive performance such as accuracy, sensitivity, specificity, F-score, Kappa index, and ROC. From the analysis results, RF was found to be the best algorithm with 100% prediction accuracy in comparison with LDA and SVM with 95.2% and 90.9% respectively. Our analysis shows that out of these three classification models RF predicts COVID-19 patient's survival outcome with the highest accuracy. Chi-square test reveals that all the seven features except sex were significantly correlated with the COVID-19 patient's outcome (P-value < 0.005). Therefore, RF was recommended for COVID-19 patient outcome prediction that will help in early identification of possible sensitive cases for quick provision of quality health care, support and supervision.
has issue date	2020-08-10(xsd:dateTime)
bibo:doi	10.1101/2020.08.02.20129767
has license	medrxiv
sha1sum (hex)	1c100b0de7f9ae33c84254c3ece093866dce16de
schema:url	https://doi.org/10.1101/2020.08.02.20129767
resource representing a document's title	A Classification Approach for Predicting COVID-19 Patient Survival Outcome with Machine Learning Techniques
resource representing a document's body	covid:1c100b0de7f9ae33c84254c3ece093866dce16de#body_text
is schema:about of	named entity 'cases' named entity 'sensitive' named entity 'Chi-square test' named entity 'features' named entity 'patient' named entity 'sensitivity' named entity 'classification models' named entity 'Survival' named entity 'COVID-19' named entity 'metrics' named entity 'early' named entity 'F-score' named entity 'Linear Discriminant Analysis' named entity 'LDA' named entity 'SVM' named entity 'algorithms' named entity 'COVID-19' named entity 'public health' named entity 'SVM' named entity 'infectious disease' named entity 'Wuhan' named entity 'SVM' named entity 'COVID-19' named entity 'algorithm' named entity 'Machine Learning Techniques' named entity 'COVID-19' named entity 'SVM' named entity 'machine learning' named entity 'Wuhan' named entity 'COVID-19' named entity 'Severe Acute Respiratory Syndrome Coronavirus 2' named entity 'LDA' named entity 'comorbidity' named entity 'supervised machine learning' named entity 'clinical outcomes' named entity 'COVID-19' named entity 'respiratory infection' named entity 'fever' named entity 'diabetes' named entity 'Support Vector Machine' named entity 'widely reported' named entity 'ALOS' named entity 'LDA' named entity 'random forest' named entity 'risk factor' named entity 'SVM' named entity 'feature space' named entity 'machine learning' named entity 'Severe Acute Respiratory Syndrome' named entity 'Linear Discriminant Analysis' named entity 'World health organization' named entity 'hyperplane' named entity 'COVID' named entity 'epidemiological' named entity 'COVID' named entity 'COVID-19' named entity 'MERS-CoV' named entity 'posterior probability' named entity 'square root' named entity 'COVID' named entity 'Random Forest' named entity 'sore throat' named entity 'kernel function' named entity 'SARS-CoV' named entity 'data analysis' named entity 'Support Vector Machines' named entity 'SVM' named entity 'SVM' named entity 'SVM' named entity 'ROC curve' named entity 'Bayes theorem' named entity 'Random Forest' named entity 'F-score'

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