Age Characteristics of Patients with Diagnosis of Leukemia in Setif-Algeria: Intelligent Modeling System
[1]
Bouharati Khaoula, Laboratory of Health and Environment, Faculty of Medicine, University Ferhat Abbas Setif1, Setif, Algeria.
[2]
Kara Lamia, Laboratory of Health and Environment, Faculty of Medicine, University Ferhat Abbas Setif1, Setif, Algeria.
[3]
Zaidi Zoubida, Laboratory of Health and Environment, Faculty of Medicine, University Ferhat Abbas Setif1, Setif, Algeria.
[4]
Bouaoud Souad, Laboratory of Health and Environment, Faculty of Medicine, University Ferhat Abbas Setif1, Setif, Algeria.
[5]
Boukhaouba Hafida, Laboratory of Health and Environment, Faculty of Medicine, University Ferhat Abbas Setif1, Setif, Algeria.
[6]
Bouharati Saddek, Intelligent Systems Laboratory, University Ferhat Abbas Setif1, Setif, Algeria.
[7]
Hamdi-Cherif Mokhtar, Laboratory of Health and Environment, Faculty of Medicine, University Ferhat Abbas Setif1, Setif, Algeria.
Leukemia is the most prevalent type of cancer among young people. Different factors intervene in its appearance. On the whole, these factors are ill-defined, imprecise and uncertain. Environmental factors are most likely such as genetic, drugs and chemicals, viral and electromagnetic radiations factor. In order to analyze these factors, several studies deal with statistical analyzes. However, these analyzes remain in the probable and far from being exact. In this study, an intelligent approach is proposed. The application of artificial intelligence tools such as fuzzy inference is proposed. Fuzzy logic deals with uncertainty and imprecision and imitates human reasoning. Its application in this field is perfectly adequate. A fuzzy system is established. Input variables (age, sex and year) are linked to a fuzzy output variable, which expresses the incidence of this type of cancer registered for nine years by the national registry of cancer of Setif in Algeria. A basis of rules is established. Considering these variables as uncertain and imprecise, the system will predict the incidence of this type of cancer with maximum accuracy.
Leukemia, Risk Factors, Artificial Intelligence, Fuzzy Logic
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