Predicting the Existence of COVID-19 using Machine Learning Based on Laboratory Findings

Hamza Turabieh, Wahiba Ben Abdessalem Karâa

202121 citationsDOI

Abstract

Since December 2019, a new coronavirus disease (COVID-19) was detected in Wuhan, China, spread all over the world. Many research papers have been published to study this disease and help humans to overcome this pandemic. Here, we highlighted the prediction process of COVID-19 based on a combination between wrapper feature selected (FS) algorithm and four different classifiers, namely Convolutional Neural Network (CNN), decision trees (C4.5), nearest neighbors (kNN) and, Naïve Bayes (NB). A real dataset has been used in this paper generated by Hospital Israelita Albert Einstein at Sao Paulo, Brazil. The obtained results show an excellent performance of BGA with CNN compared to other methods with accuracy 76%.

Topics & Concepts

Convolutional neural networkNaive Bayes classifierCoronavirus disease 2019 (COVID-19)Artificial intelligenceComputer scienceMachine learningPandemicDecision treeFeature (linguistics)Bayes' theoremArtificial neural networkPattern recognition (psychology)DiseaseBayesian probabilitySupport vector machineInfectious disease (medical specialty)MedicineLinguisticsPhilosophyPathologyCOVID-19 diagnosis using AIAnomaly Detection Techniques and ApplicationsArtificial Intelligence in Healthcare