Int. J. Biomedical Engineering and Technology, Vol. 18, No. 4, 2015 359 Copyright © 2015 Inderscience Enterprises Ltd. A rough set based data model for breast cancer mammographic mass diagnostics Aaron Don M. Africa* and Melvin K. Cabatuan Electronics and Communications Engineering Department, De La Salle University, Manila, Philippines Email: aaron.africa@dlsu.edu.ph Email: melvin.cabatuan@dlsu.edu.ph *Corresponding author Abstract: Breast cancer is the principal cause of cancer deaths among women, and early diagnosis is critical to its survival. Mammography is the recommended diagnostic procedure for ages 40 years and older. However, the low precision rate of mammographic result leads to needless biopsies. Thus, in this paper, we present the application of rough set theory in the development of a data model to aid in physician’s recommendation for biopsy. In particular, we will utilise the data obtained at the Institute of Radiology of the University Erlangen-Nuremberg between 2003 and 2006. The results showed that the rough set approach successfully reduced the dimensionality of the aforementioned data set by approximately 47%, and the outcome rules were validated using empirical testing at 100%. Keywords: rough set theory; breast cancer; biomedical engineering; decision support systems. Reference to this paper should be made as follows: Africa, A.D.M. and Cabatuan, M.K. (2015) ‘A rough set based data model for breast cancer mammographic mass diagnostics’, Int. J. Biomedical Engineering and Technology, Vol. 18, No. 4, pp.359–369. Biographical notes: Aaron Don M. Africa earned his BS degree in Electronics and Communications Engineering (ECE) from the University of Santo Tomas. He completed his Masters degree in ECE at the De La Salle University Manila. Presently, he is an Associate Professor of De La Salle University Manila and a PhD graduate in ECE of the said school. Melvin K. Cabatuan received the BSc degree in Electronics and Communications Engineering from Cebu Institute of Technology University, Cebu, Philippines, in 2004; and MS degree in Engineering from NAIST located at Ikoma, Nara, Japan, in 2010. He joined the Electronics & Communications Engineering department of De La Salle University in 2011, where he is currently an Assistant Professor. His research interest involves machine learning applications to health informatics.