Breast cancer is becoming the major factor of death amongst women in the world. However, it is found that longer lifespan of this disease's patients can be guaranteed through early detection and accurate diagnosis of this disease. When it comes to the treatment given to patients, a doctor needs to put his/her knowledge and experience in practice by specifying the source of the suspected disease (out of a list of the possible causes with similar symptoms). This is followed by confirming the diagnosis through a number of tests. Therefore, identifying the disease without receiving assistance from intelligence systems is time consuming. The objective of this study is to introduce the intelligence system which develops Random Binary Search algorithm-based feature selection in Mahalanobis Taguchi System (MTS). It is also with the purpose to validate the techniques of feature selection problems which are computationally efficient, and to apply Random Binary Search algorithm in solving medical classification problems. In this study, in order to improve the step of choosing the most useful variables, Random Binary Search (RBS) algorithm is proposed, which is incorporated between MTS. Besides being a relatively new statistical methodology where various mathematical concepts are combined, MTS is used in the field of diagnosis and classification in multidimensional systems. It is also a highly efficient method, and it has been utilized in a wide range of disciplines such as engineering, medical, financial, and more. Datasets of medical fields, which were concerning cancer, diabetes and hepatitis, were used in this study. Besides, binary class classification problems were also represented by these data sets.
