Volume 51, Issue 1, October 2020, Pages 45–52
David Kutangila Mayoya1 and Deny Botha Matuba2
1 Université Pédagogique Nationale, RD Congo
2 Université Pédagogique Nationale, RD Congo
Original language: French
Copyright © 2020 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
We build an expert system for the tropical diseases diagnosis based on fuzzy symptoms. In effect, for a given disease, each symptom is assigned a weight indicating its belonging degree to the fuzzy set of symptoms that determine that disease. So, from a set of fuzzy symptoms, the expert system determines a disease by aggregating symptoms weights to calculate the certainty degree of the diagnosis realized.
Author Keywords: Artificial Intelligence, Expert System, fuzzy subset.
David Kutangila Mayoya1 and Deny Botha Matuba2
1 Université Pédagogique Nationale, RD Congo
2 Université Pédagogique Nationale, RD Congo
Original language: French
Copyright © 2020 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
We build an expert system for the tropical diseases diagnosis based on fuzzy symptoms. In effect, for a given disease, each symptom is assigned a weight indicating its belonging degree to the fuzzy set of symptoms that determine that disease. So, from a set of fuzzy symptoms, the expert system determines a disease by aggregating symptoms weights to calculate the certainty degree of the diagnosis realized.
Author Keywords: Artificial Intelligence, Expert System, fuzzy subset.
Abstract: (french)
Nous développons un système expert pour le diagnostic des maladies tropicales sur base des symptômes flous. En effet, pour une maladie donnée, chaque symptôme reçoit une pondération qui indique son degré d’appartenance à l’ensemble flou des symptômes déterminant cette maladie. Ainsi à partir d’un ensemble de symptômes flous, le système expert détermine une maladie en agrégeant les pondérations des symptômes pour déterminer le niveau de certitude du diagnostic réalisé.
Author Keywords: Intelligence artificielle, système expert, sous-ensemble flou.
How to Cite this Article
David Kutangila Mayoya and Deny Botha Matuba, “Système expert flou pour le diagnostic symptomatique des maladies tropicales : Cas de la malaria et de la fièvre typhoïde,” International Journal of Innovation and Scientific Research, vol. 51, no. 1, pp. 45–52, October 2020.
