The accuracy of methods for the assessment of mammographic risk analysis is heavily related to breast tissue characteristics. Previous work has demonstrated considerable success in developing an automatic breast tissue classification methodology which overcomes this difficulty. This paper proposes a unified approach for the application of a number of rough and fuzzy-rough set methods to the analysis of mammographic data. Indeed this is the first time that fuzzy-rough approaches have been applied to this particular problem domain. In the unified approach detailed here feature selection methods are employed for dimensionality reduction developed using rough sets and fuzzy-rough sets. A number of classifiers are then used to examine the data reduced by the feature selection approaches and assess the positive impact of these methods on classification accuracy. Additionally, this paper also employs a new fuzzy-rough classifier based on the nearest neighbour classification algorithm. The novel use of such an approach demonstrates its efficiency in improving classification accuracy for mammographic data, as well as considerably removing redundant, irrelevant, and noisy features. This is supported with experimental application to two well-known datasets. The overall result of employing the proposed unified approach is that feature selection can identify only those features which require extraction. This can have the positive effect of increasing the risk assessment accuracy rate whilst additionally reducing the time required for expert scrutiny, which in-turn means the risk analysis process is potentially quicker and involves less screening.