{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T09:24:10Z","timestamp":1760606650318},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2021,6,3]],"date-time":"2021-06-03T00:00:00Z","timestamp":1622678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,16]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Since recording the first case in Wuhan in November 2020, COVID-19 is still spreading widely and rapidly affecting the health of millions all over the globe. For fighting against this pandemic, numerous strategies have been made, where the early isolation is considered among the most effective ones. Proposing useful methods to screen and diagnose the patient\u2019s situation for the purpose of specifying the adequate clinical management represents a significant challenge in diminishing the rates of mortality. Inspired from this current global health situation, we introduce a new autonomous process of decision-making that consists of two modules. The first module is the data analysis based on Bayesian network that is employed to indicate the coronavirus symptoms severity and then classify COVID-19 cases as severe, moderate or mild. The second module represents the decision-making based on association rules method that generates autonomously the adequate decision. To construct the model of Bayesian network, we used an effective method-oriented data for the sake of learning its structure. As a result, the algorithm accuracy in making the correct decision is 30% and in making the adequate decision is 70%. These experimental results demonstrate the importance of the suggested methods for decision-making.<\/jats:p>","DOI":"10.1093\/comjnl\/bxab071","type":"journal-article","created":{"date-parts":[[2021,5,7]],"date-time":"2021-05-07T11:38:08Z","timestamp":1620387488000},"page":"2360-2376","source":"Crossref","is-referenced-by-count":4,"title":["A Novel Decision-Making Process for COVID-19 Fighting Based on Association Rules and Bayesian Methods"],"prefix":"10.1093","volume":"65","author":[{"given":"Salim","family":"El Khediri","sequence":"first","affiliation":[{"name":"Department of Information Technology , College of Computer, , Buraydah 51542, Saudi Arabia"},{"name":"Qassim University , College of Computer, , Buraydah 51542, Saudi Arabia"},{"name":"Department of Computer Sciences , Faculty of Sciences of Gafsa, , Gafsa 2112, Tunisia"},{"name":"University of Gafsa , Faculty of Sciences of Gafsa, , Gafsa 2112, Tunisia"},{"name":"LETI Laboratory , , National School of Engineers (ENIS), BP 1173, Sfax 3038, Tunisia"},{"name":"University of Sfax , , National School of Engineers (ENIS), BP 1173, Sfax 3038, Tunisia"}]},{"given":"Adel","family":"Thaljaoui","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information , College of Science at Zulfi, , Al-Majmaah 11952, Saudi Arabia"},{"name":"Majmaah University , College of Science at Zulfi, , Al-Majmaah 11952, Saudi Arabia"}]},{"given":"Fayez","family":"Alfayez","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information , College of Science at Zulfi, , Al-Majmaah 11952, Saudi Arabia"},{"name":"Majmaah University , College of Science at Zulfi, , Al-Majmaah 11952, Saudi Arabia"}]}],"member":"286","published-online":{"date-parts":[[2021,6,3]]},"reference":[{"key":"2022091610475049900_ref1","author":"Coronavirus disease (COVID-19) pandemic"},{"key":"2022091610475049900_ref2","first-page":"1","article-title":"Lymphopenia predicts disease severity of COVID-19: a descriptive and predictive study","volume":"5","author":"Tan","year":"2020","journal-title":"Signal Transduct. Target. Ther."},{"key":"2022091610475049900_ref3","article-title":"A deep learning algorithm using CT images to screen for corona virus disease (COVID-19)","author":"Wang","year":"2020","journal-title":"medRxiv"},{"key":"2022091610475049900_ref4","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.2020200642","article-title":"Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases","author":"Ai","year":"2020","journal-title":"Radiology"},{"key":"2022091610475049900_ref5","doi-asserted-by":"crossref","first-page":"E65","DOI":"10.1148\/radiol.2020200905","article-title":"Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT","volume":"296","author":"Li","year":"2020","journal-title":"Radiology"},{"key":"2022091610475049900_ref6","article-title":"Rapid AI development cycle for the coronavirus (covid-19) pandemic: initial results for automated detection & patient monitoring using deep learning CT image analysis","author":"Gozes","year":"2020","journal-title":"arXiv preprint"},{"key":"2022091610475049900_ref7","article-title":"COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images","author":"Wang","year":"2020","journal-title":"arXiv preprint"},{"key":"2022091610475049900_ref8","doi-asserted-by":"crossref","first-page":"674","DOI":"10.3390\/jcm9030674","article-title":"Optimization method for forecasting confirmed cases of COVID-19 in China","volume":"9","author":"Al-Qaness","year":"2020","journal-title":"J. Clin. Med."},{"key":"2022091610475049900_ref9","doi-asserted-by":"crossref","first-page":"2427","DOI":"10.3390\/su12062427","article-title":"Investigating a serious challenge in the sustainable development process: analysis of confirmed cases of COVID-19 (new type of coronavirus) through a binary classification using artificial intelligence and regression analysis","volume":"12","author":"Pirouz","year":"2020","journal-title":"Sustainability"},{"key":"2022091610475049900_ref10","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.eswa.2019.04.060","article-title":"A hybrid approach for identifying the structure of a Bayesian network model","volume":"131","author":"Huang","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"2022091610475049900_ref11","doi-asserted-by":"crossref","first-page":"1382","DOI":"10.1109\/TSMCB.2011.2148197","article-title":"A method for integrating expert knowledge when learning Bayesian networks from data","volume":"41","author":"Cano","year":"2011","journal-title":"IEEE Trans. Syst. Man Cybern. B Cybern."},{"key":"2022091610475049900_ref12","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.jbi.2013.10.012","article-title":"Not just data: a method for improving prediction with knowledge","volume":"48","author":"Yet","year":"2014","journal-title":"J. Biomed. Inform."},{"key":"2022091610475049900_ref13","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.ijar.2006.06.009","article-title":"Bayesian network learning algorithms using structural restrictions","volume":"45","author":"De Campos","year":"2007","journal-title":"Int. J. Approx. Reason."},{"key":"2022091610475049900_ref14","doi-asserted-by":"crossref","first-page":"2154","DOI":"10.1109\/TPAMI.2016.2636828","article-title":"Exploiting experts\u2019 knowledge for structure learning of Bayesian networks","volume":"39","author":"Amirkhani","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2022091610475049900_ref15","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.jenvman.2015.09.024","article-title":"Risk analysis of emergent water pollution accidents based on a Bayesian network","volume":"165","author":"Tang","year":"2016","journal-title":"J. Environ. Manag."},{"key":"2022091610475049900_ref16","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.knosys.2018.03.007","article-title":"Novel binary encoding water cycle algorithm for solving Bayesian network structures learning problem","volume":"150","author":"Wang","year":"2018","journal-title":"Knowl.-Based Syst."},{"key":"2022091610475049900_ref17","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.patrec.2018.04.019","article-title":"Finding a set of candidate parents using dependency criterion for the K2 algorithm","volume":"111","author":"Tabar","year":"2018","journal-title":"Pattern Recogn. Lett."},{"key":"2022091610475049900_ref18","doi-asserted-by":"crossref","first-page":"303","DOI":"10.3390\/e19070303","article-title":"Node importance ranking of complex networks with entropy variation","volume":"19","author":"Ai","year":"2017","journal-title":"Entropy"},{"key":"2022091610475049900_ref19","doi-asserted-by":"crossref","first-page":"2423","DOI":"10.1080\/00207160.2019.1566535","article-title":"A novel discrete particle swarm optimization algorithm for solving Bayesian network structures learning problem","volume":"96","author":"Wang","year":"2019","journal-title":"Int. J. Comput. Math."},{"key":"2022091610475049900_ref20","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.ins.2016.01.090","article-title":"Bnc-pso: structure learning of bayesian networks by particle swarm optimization","volume":"348","author":"Gheisari","year":"2016","journal-title":"Inf. Sci."},{"key":"2022091610475049900_ref21","first-page":"1287","article-title":"Large-sample learning of Bayesian networks is NP-hard","volume":"5","author":"Chickering","year":"2004","journal-title":"J. Mach. Learn. Res."},{"key":"2022091610475049900_ref22","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1016\/j.eswa.2018.06.058","article-title":"An improved constraint-based Bayesian network learning method using Gaussian kernel probability density estimator","volume":"113","author":"Jiang","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"2022091610475049900_ref23","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1007\/s11227-018-2297-6","article-title":"A parallel FP-Growth algorithm on World Ocean Atlas data with multi-core CPU","volume":"75","author":"Jiang","year":"2019","journal-title":"J. Supercomput."},{"key":"2022091610475049900_ref24","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1007\/s00607-015-0457-6","article-title":"A fast and distributed algorithm for mining frequent patterns in congested networks","volume":"98","author":"Lin","year":"2016","journal-title":"Computing"},{"key":"2022091610475049900_ref25","first-page":"487","volume-title":"Proceedings of 20th International Conference on very large data bases, VLDB","author":"Agrawal","year":"1994"},{"key":"2022091610475049900_ref26","first-page":"56","volume-title":"9th International Symposium on Foundations, Tools, and Application, RuleML 2015","author":"F\u00fcrnkranz","year":"2015"},{"key":"2022091610475049900_ref27","first-page":"321","article-title":"Parallel implementation of apriori algorithms on the Hadoop-MapReduce platform-an evaluation of literature","volume":"85","author":"Saabith","year":"2016","journal-title":"J. Theor. Appl. Inf. Technol."},{"key":"2022091610475049900_ref28","doi-asserted-by":"crossref","first-page":"960","DOI":"10.18517\/ijaseit.9.3.7025","article-title":"Automatic rule generator via FP-Growth for eye diseases diagnosis","volume":"9","author":"Kurniawan","year":"2019","journal-title":"Int. J. Adv. Sci. Eng. Inform. Technol."},{"key":"2022091610475049900_ref29","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1109\/ICSESS.2013.6615467","volume-title":"Proceedings of 2013 IEEE 4th International Conference on Software Engineering and Service Science (ICSESS)","author":"Rong","year":"2013"},{"key":"2022091610475049900_ref30","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1109\/CC.2016.7559082","article-title":"Multilevel pattern mining architecture for automatic network monitoring in heterogeneous wireless communication networks","volume":"13","author":"Qu","year":"2016","journal-title":"China Commun."},{"key":"2022091610475049900_ref31","first-page":"458","volume-title":"2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)","author":"Dong","year":"2019"},{"key":"2022091610475049900_ref32","volume-title":"Proceeding. IEEE ISKE, the 14th International Conference on Intelligent Systems and Knowledge Engineering","author":"Benmohamed","year":"2019"},{"key":"2022091610475049900_ref33"},{"key":"2022091610475049900_ref34","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1016\/j.jiph.2020.03.019","article-title":"Coronavirus disease 2019 (COVID-19): a literature review","volume":"13","author":"Harapan","year":"2020","journal-title":"J. Infect. Public Health"},{"key":"2022091610475049900_ref35","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/S0140-6736(20)30183-5","article-title":"Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China","volume":"395","author":"Huang","year":"2020","journal-title":"Lancet"},{"key":"2022091610475049900_ref36","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/S0140-6736(20)30211-7","article-title":"Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study","volume":"395","author":"Chen","year":"2020","journal-title":"Lancet"},{"key":"2022091610475049900_ref37","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.2020200230","article-title":"CT imaging features of 2019 novel coronavirus (2019-nCoV)","author":"Chung","year":"2020","journal-title":"Radiology"},{"key":"2022091610475049900_ref38","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.patrec.2013.12.021","article-title":"An efficient node ordering method using the conditional frequency for the K2 algorithm","volume":"40","author":"Ko","year":"2014","journal-title":"Pattern Recogn. Lett."},{"key":"2022091610475049900_ref39","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1109\/CEC.2017.7969546","volume-title":"2017 IEEE Congress on Evolutionary Computation (CEC)","author":"De Stefano","year":"2017"},{"key":"2022091610475049900_ref40","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1007\/s00500-012-0966-6","article-title":"An artificial bee colony algorithm for learning Bayesian networks","volume":"17","author":"Ji","year":"2013","journal-title":"Soft. Comput."},{"key":"2022091610475049900_ref41","article-title":"Learning Bayesian network structure from massive datasets: the \u201csparse candidate\u201d algorithm","author":"Friedman","year":"2013","journal-title":"arXiv preprint"},{"key":"2022091610475049900_ref42","first-page":"711","article-title":"A novel algorithm for scalable and accurate Bayesian network learning","author":"Brown","year":"2004","journal-title":"Medinfo"}],"container-title":["The Computer Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/comjnl\/article-pdf\/65\/9\/2360\/45882328\/bxab071.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/comjnl\/article-pdf\/65\/9\/2360\/45882328\/bxab071.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T10:50:30Z","timestamp":1663325430000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/comjnl\/article\/65\/9\/2360\/6291513"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,3]]},"references-count":42,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2021,6,3]]},"published-print":{"date-parts":[[2022,9,16]]}},"URL":"https:\/\/doi.org\/10.1093\/comjnl\/bxab071","relation":{},"ISSN":["0010-4620","1460-2067"],"issn-type":[{"value":"0010-4620","type":"print"},{"value":"1460-2067","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,9]]},"published":{"date-parts":[[2021,6,3]]}}}