Introduction. - Job retention has become a priority in terms of occupational health. The risk of unfitness for work due to medical reasons is the main risk of professional disinsertion with health consequences. The objective is to create a "sector-job retention/disability matrix" as indicators of sectors at risk, using data from the local observatory on the island of Reunion. Method. - A retrospective study of data collected between 2017 and 2021 by the two occupa-tional health and prevention services on Reunion Island (Intermetra and SISTBI, excluding the civil service), was conducted. According to the activity sector classified by the French Nomen-clature of Activities 2008 (NAF), the number of incapacities per year and their proportion was described, as well as the number of employees benefiting from the obligation of employment of disabled worker "OETH" among these incapacities. Descriptive and predictive analyses using Machine Learning were performed. Results. - Between 2017 and 2021, an increase in incapacities and disabled worker employment obligations was observed on the observatory data. The main sectors, which are accommoda-tion and catering, human health and social action (including the home help sector), certain businesses such as food sales, the construction sector with masonry work, and the cleaning sector are at risk of professional displacement on these data in classic analysis. The creation of Machine Learning (in particular Elastic Net, Random Forest, Decision Tree) makes it possible to make predictions that must be verified over the following years. Conclusion. - In conclusion, this original approach by "sector-job retention/disability matrix"highlights the importance of the use of remarkable data collected daily by the occupational physicians of the Reunion Island in the framework of the prevention of job loss.