Integrated Bayesian and association-rules methods for autonomously orienting COVID-19 patients.
The coronavirus infection continues to spread rapidly worldwide, having a devastating impact on the health of the global population. To fight against COVID-19, we propose a novel autonomous decision-making process which combines two modules in order to support the decision-maker: (1) Bayesian Networ...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 60; no. 12; pp. 3475 - 3497 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Dec2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160112194&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160112194 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Dec2022 vid: 60 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 160112194 160112194 NLM36205834 10.1007/s11517-022-02677-y NLM36205834 160112194 ppf: 3475 ppct: 22 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Integrated Bayesian and association-rules methods for autonomously orienting COVID-19 patients. aug: au: Thaljaoui, Adel Khediri, Salim El Benmohamed, Emna Alabdulatif, Abdulatif Alourani, Abdullah affil: Department of Computer Science and Information, College of Science at Zulfi, Majmaah University, 11952, Al-Majmaah, Saudi Arabia sug: ab: The coronavirus infection continues to spread rapidly worldwide, having a devastating impact on the health of the global population. To fight against COVID-19, we propose a novel autonomous decision-making process which combines two modules in order to support the decision-maker: (1) Bayesian Networks method-based data-analysis module, which is used to specify the severity of coronavirus symptoms and classify cases as mild, moderate, and severe, and (2) autonomous decision-making module-based association rules mining method. This method allows the autonomous generation of the adequate decision based on the FP-growth algorithm and the distance between objects. To build the Bayesian Network model, we propose a novel data-based method that enables to effectively learn the network's structure, namely, MIGT-SL algorithm. The experimentations are performed over pre-processed discrete dataset. The proposed algorithm allows to correctly generate 74%, 87.5%, and 100% of the original structure of ALARM, ASIA, and CANCER networks. The proposed Bayesian model performs well in terms of accuracy with 96.15% and 94.77%, respectively, for binary and multi-class classification. The developed decision-making model is evaluated according to its utility in solving the decisional problem, and its accuracy of proposing the adequate decision is about 97.80%. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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