In this study, researchers propose a novel Automatic Supervised Feature Selection and Discretization algorithm to enhance the classification of medical images (mammograms). The proposed method consists of a new algorithm called, NANO for a filter based supervised feature selection and discretization. This algorithm solves two problems, viz., feature discretization and selection in a single step. An important contribution of the proposed algorithm is the reduction of irrelevant Items to be mined. NANO selects the relevant features based on the average global inconsistency and average global cut point measures, speeding up the medical image diagnosis framework. Two set of experiments have been performed to validate the proposed method. Experiments are carried out to validate the performance of NANO algorithm in the task of feature selection and discretization. Performance evaluation was done for the first experiments using precision and recall metrics obtained from the query and retrieved images. The second set of experiments aim at validating the classification accuracy. From the experiments, it is observed that the proposed method shows high sensitivity (up to 98.64%) and high accuracy (up to 96.95%). © Medwell lournals, 2015.