According to the World Health Organization, starting from 2010, cancer will become the leading cause of death worldwide. Prevention of major cancer localizations through a quantified assessment of risk factors is a major concern in order to decrease their impact in our society. Our objective is to test the performances of a modeling method easily readable by a physician. In this article, we follow a data mining process to build a reliable assessment tool for primary breast cancer risk. A k-nearest-neighbor algorithm is used to compute a risk score for different profiles from a public database. We empirically show that it is possible to achieve the same performances than logistic regressions with less parameters and a more easily readable model. The process includes the intervention of a domain expert who helps to select one of the numerous model variations by combining at best, physician expectations and performances. A risk score is made up of four parameters: age, breast density, number of affected first degree relatives and prone to breast biopsy. Detection performance measured with the area under the ROC curve is 0.637.