A rough set based data model for breast cancer mammographic mass diagnostics

• Main Authors: Africa, Aaron Don M., Cabatuan, Melvin K.
• Format: text
• Published: Animo Repository 2015
• Subjects: Biomedical engineering; Breast—Cancer—Diagnosis; Rough sets; Decision support systems; Biomedical Engineering and Bioengineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2300; https://animorepository.dlsu.edu.ph/context/faculty_research/article/3299/type/native/viewcontent
• Institution: De La Salle University

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%. Copyright © 2015 Inderscience Enterprises Ltd.