This article presents a study on the demographic and socio-economic profile of sub-Saharan migrants in an irregular situation in the Laayoune City. We then analyze the reasons and reasons for migration. A questionnaire was prepared for this study and randomly distributed to sub-Saharan immigrants. The data obtained is analyzed by means of a software program: (Sphinx Lexica 2000). The study showed that the average age of the studied population is 29 years of which 20% are women, the majority of sub-Saharan migrants in Laayoune are of Senegalese origin followed by Malians, this young population decided to stop their studies by working to finance their migration project, at the interpersonal level. As concerns the socio-professional category, the study showed that 62.42% are workers and 35, 71% are unemployed. Finally, the possibility of finding work more easily in the country of final destination and poverty remain two factors that encourage immigrants to leave their origin countries.

In this article, we focus our work on the modeling of genetic risk factors on ischemic strokes occurred. To do this, logistic regression was widespread in our study. We proceeded in two stages: the first, we modeled the probability of the occurrence of ischemic stroke in an individual (i) based on genetic risk factors. Our sample consisted of 330 individuals aged at least 40 years, divided into 165 patients who had an ischemic stroke and 165 controls.
We applied the Wald test for all variables in the model one by one and we concluded to Reject H0, since the coefficients of our variables are not all zero.
In a second step, we studied the effects of these variables on the risk factors and then the effect of variables on Ischemic stroke to present the model equation.
We set a prediction threshold after specification test, that allows us to ensure the quality of the fit of the model and its degree of prediction, the proportion of people who have ischemic stroke is (50%). The results showed that 128 of 156 people with Ischemic stroke allowed a positive predictive value of 82%. We conclude that the prediction rate and the success rate of our model is 80.30 %, the results obtained with the «XLSTAT» software show a very good model with (sensitivity 78% and specificity 83%).