Background: From the epidemiological point of view, certain factors involved in the appearance of varicose veins are preponderant such as multiple pregnancies, age, and also certain races. Physiologically, venous valve dysfunction can also be a factor. Here, radiologists intervene to determine venous insufficiency. Doppler ultrasound (US) and tomography are often the most used in this detection. Certain other factors contribute to their recidivism. Aims: Some factors that occur in the recurrence of varicose veins are extrinsic such as age, sex, or genetic factor. On the other hand, certain factors are linked to an inadequate surgical procedure that can be partly explained by a poor radiological or methodological reading. The aim of this study is to prevent recurring complications that may occur the analysis of the factors of these is necessary. Materials and Methods: In our study, 62 patients were operated in our general surgery department during the period from January 2016 to September 2017. The pre-operative clinical examination included, among others, the radiological examination using a Doppler US. Patients who have had a recurrence are classified from the identification of the possible causes. Since the causes are complex and vary from one person to another, this makes them very difficult to analyze by conventional methods. We proposed an intelligent system based on artificial neural networks. Results: Once the system is established, this will identify the most important factor in the recurrence of varicose veins. By randomly changing the parameters at the input one by one and we record the effect that each produces on the recurrence rate at the output. Conclusion: The proposed system with its very strong inters connectivity, and the support of all possible combinations with the weight of each factor makes it possible to extract the predominant cause. With its learning from the real values recorded, and the optimal function created between the two input-output spaces, it becomes very easy to identify the main cause that leads to recidivism.

Background. It is evident that the B hepatitis disease is favored by several risk factors. Among the factors analyzed in this study, gender, diabetes, arterial hypertension, and body mass index. The age of the first infection is related to these variables. As the system is very complex, because other factors can have an effect and which are ignored, this study processes data using artificial intelligence techniques. Method. The study concerns 30 patients diagnosed at our service of the university hospital of Setif in Algeria. The study period runs from 2011 to 2020. The risk factors are considered imprecise and therefore fuzzy. A fuzzy inference system is applied in this study. The data is fuzzyfied and a rule base is established. Results. As the principles of fuzzy logic deal with the uncertain, this allowed us to take care of this imprecision and complexity. The established rule base maps the inputs, which are the risk factors, to hepatitis as the output variable. Conclusion. Several factors promote hepatitis B. The physiological system differs from one individual to another. Also, the weight of each factor is ignored. Given this complexity, the principles of fuzzy logic proposed are adequate. Once the system has been completed, it allows the random introduction of values at the input to automatically read the result at the output. This tool can be considered as a prevention system in the appearance and and establish a typical profile of people likely to be affected by hepatitis.

BACKGROUND: Several factors are at the root of the aneurysm. This disease is characterized by the loss of parallelism of the abdominal aorta, which widens to a breaking point. This anomaly is silent, and it is discovered by accident during the diagnosis of another patient. High blood pressure is often associated with the aneurysm. The age factor is directly linked to other factors that are often poorly understood. SUBJECTS AND METHODS: This study proposes the analysis of these factors by artificial intelligence techniques, in particular fuzzy analysis. This mode of reasoning compensates for the uncertainties inherent in the process. A fuzzy system is established, allowing factors recorded from 100 diagnosed patients to be entered as input variables to the system to read the predicted aortic diameter. RESULTS: The result at the output of the system will be as precise as possible, because, it is calculated from the aggregation of all the variables. This study compensates for imprecision and uncertainty by considering blood pressure as an imprecise and therefore fuzzy variable CONCLUSIONS: The result at the output of the system will be as precise as possible, because it is calculated from the aggregation of all the variables. In the absence of a systematic screening program, this system can be a tool to help prevent this disease.

Background: Clinical examination and endoscopic biopsy are often not sufficient in the analysis of the evolution of laryngeal cancers. The imaging extension of laryngeal tumors is based on the helical multiband scanner. Aims: Some deep extensions of the initial tumor pathology are only accessible using imaging. This is decisive in the therapeutic choice. It is the same in the case of ganglionic extensions in the management of lymphocytic tumors. The precise identification of these is necessary in the therapeutic orientation. Materials and Methods: In our study, we give an overview of the imaging techniques used in the follow-up of 100 patients at the University Hospital of Setif in Algeria, and the impact of our imaging results on the prognosis and clinical management of these patients. Results: From the results obtained in imaging of these patients, there are findings relating to volumes, shapes, density, location, and extension. These variations are very complex to analyze using conventional mathematical tools. We propose a system that uses artificial intelligence techniques for their analysis. Conclusion: A fuzzy inference system is proposed. The input variables of the system express the tumor characteristics that are mapped to the output variable that expresses the therapeutic protocol to be adopted.