Using a genetic-fuzzy algorithm as a computer aided diagnosis tool on Saudi Arabian breast cancer database

• Published in: Mathematical biosciences Vol. 286; pp. 39 - 48
• Main Authors: Alharbi, Abir, Tchier, F.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.04.2017; Elsevier Science Ltd
• Subjects: Algorithms; Breast cancer; Breast Neoplasms - diagnosis; Cancer; Computer-aided diagnosis; Databases, Factual; Diagnosis; Diagnosis, Computer-Assisted; Evolutionary algorithms; Fuzzy Logic; Fuzzy systems; Genetic algorithms; Humans; Medical diagnosis; Medical personnel; Medical research; Models, Genetic; Optimization methods; Physicians; Saudi Arabia; Scientific papers; Studies; Saudi Arabia; 68T01; 03B52; 68T20; Optimization methods; Breast cancer; 65Y10; Computer-aided diagnosis; Fuzzy systems; Genetic algorithms
• ISSN: 0025-5564; 1879-3134
• DOI: 10.1016/j.mbs.2017.02.002

The computer-aided diagnosis has become one of the major research topics in medical diagnostics. In this research paper, we focus on designing an automated computer diagnosis by combining two major methodologies, namely the fuzzy base systems and the evolutionary genetic algorithms and applying them to the Saudi Arabian breast cancer diagnosis database, to be employed for assisting physicians in the early detection of breast cancers, and hence obtaining an early-computerized diagnosis complementary to that by physicians. Our hybrid algorithm, the genetic-fuzzy algorithm, has produced optimized diagnosis systems that attain high classification performance, in fact, our best three rule system obtained a 97% accuracy, with simple and well interpretive rules, and with a good degree of confidence of 91%.