Capturing requirements in machine learning projects is a challenging task. It requires domain knowledge as well as experience in the machine learning field. The i* framework is a popular high abstraction-layer requirements capturing tool. However, the use of i* directly in the machine learning field (ML) is unfeasible due to it cannot capture all the restrictions and relationships of ML elements. In previous works we have extended i* to better capture machine learning requirements. In this paper, we apply the i* for machine learning extension to a real machine learning case study, in the context of a project focused on the diagnosis and treatment of Attention-Deficit/Hyperactivity Disorder (ADHD). The results show that the use of the i* for machine learning extension provides insights about the correct path to follow, aiding in the definition and selection of machine learning solutions that better fulfill the project requirements. Moreover, it facilitates faster development of the machine learning solution in a more structured way, avoiding errors and making the application of i* an effective tool for managing machine learning requirements
