Your browser doesn't support javascript.
Integrated Bayesian and association-rules methods for autonomously orienting COVID-19 patients.
Thaljaoui, Adel; Khediri, Salim El; Benmohamed, Emna; Alabdulatif, Abdulatif; Alourani, Abdullah.
  • Thaljaoui A; Department of Computer Science and Information, College of Science at Zulfi, Majmaah University, Al-Majmaah, 11952, Saudi Arabia.
  • Khediri SE; Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia. salim.el.khediri@gmail.com.
  • Benmohamed E; Department of Computer Sciences, Faculty of Sciences of Gafsa, University of Gafsa, Gafsa, Tunisia. salim.el.khediri@gmail.com.
  • Alabdulatif A; Department of Computer Sciences, Faculty of Sciences of Gafsa, University of Gafsa, Gafsa, Tunisia.
  • Alourani A; Research Groups in Intelligent Machines, University of Sfax, National School of Engineers (ENIS), BP 1173, 3038, Sfax, Tunisia.
Med Biol Eng Comput ; 60(12): 3475-3496, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-2060011
ABSTRACT
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%.
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Prognostic study Limits: Humans Language: English Journal: Med Biol Eng Comput Year: 2022 Document Type: Article Affiliation country: S11517-022-02677-y

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Prognostic study Limits: Humans Language: English Journal: Med Biol Eng Comput Year: 2022 Document Type: Article Affiliation country: S11517-022-02677-y