Consistent knowledge discovery in medical diagnosis

• Published in: IEEE engineering in medicine and biology magazine 2000-07, Vol.19 (4), p.26-37
• Main Authors: Kovalerchuk, B., Vityaev, E., Ruiz, J.F.
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Biomedical Engineering; Biomedical imaging; Breast cancer; Breast Neoplasms - diagnosis; Breast Neoplasms - diagnostic imaging; Data mining; Databases, Factual; Diagnosis, Computer-Assisted; Expert Systems; Female; Humans; Independent component analysis; Mammography; Medical diagnosis; Medical diagnostic imaging; Shape; Uncertainty
• ISSN: 0739-5175; 1937-4186
• DOI: 10.1109/51.853479

Discusses eliminating contradictions among rules in computer-aided systems, experts rules, and databases. The study has demonstrated how consistent data mining in medical diagnosis can create a set of logical diagnostic rules for computer-aided diagnostic systems. Consistency avoids contradiction among rules generated using data mining software, rules used by an experienced radiologist, and a database of pathologically confirmed cases. The authors identified major problems: to find contradiction between diagnostic rules and to eliminate contradiction. They applied two complimentary intelligent technologies for extraction of rules and recognition of their contradictions. The first technique is based on discovering statistically significant logical diagnostic rules. The second technique is based on the restoration of a monotone Boolean function to generate a minimal dynamic sequence of questions to a medical expert. The results of this mutual verification of expert and data-driven rules demonstrate feasibility of the approach for designing consistent computer-aided diagnostic systems.