Evolving rule-based systems in two medical domains using genetic programming

  • Authors:

  • Athanasios Tsakonas;Georgios Dounias;Jan Jantzen;Hubertus Axer;Beth Bjerregaard;Diedrich Graf von Keyserlingk

  • Affiliations:

  • Department of Financial and Management Engineering, University of the Aegean, 31 Fostini St., 82100 Chios, Greece;Department of Financial and Management Engineering, University of the Aegean, 31 Fostini St., 82100 Chios, Greece;Technical University of Denmark, Oersted-DTU Automation, Dk-2800 Kongens Lyngby, Denmark;Department of Neurology, Friedrich-Schiller-University Jena, Philosophenweg 3, D-07743 Jena, Germany;Herlev University Hospital, DK-2730 Herlev, Denmark;Department of Anatomy I, RWTH Aachen, Pauwelsstr. 30, D-52057 Aachen, Germany

  • Venue:
  • Artificial Intelligence in Medicine
  • Year:
  • 2004

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Abstract

Objective: To demonstrate and compare the application of different genetic programming (GP) based intelligent methodologies for the construction of rule-based systems in two medical domains: the diagnosis of aphasia's subtypes and the classification of pap-smear examinations.Material: Past data representing (a) successful diagnosis of aphasia's subtypes from collaborating medical experts through a free interview per patient, and (b) correctly classified smears (images of cells) by cyto-technologists, previously stained using the Papanicolaou method.Methods: Initially a hybrid approach is proposed, which combines standard genetic programming and heuristic hierarchical crisp rule-base construction. Then, genetic programming for the production of crisp rule based systems is attempted. Finally, another hybrid intelligent model is composed by a grammar driven genetic programming system for the generation of fuzzy rule-based systems.Results: Results denote the effectiveness of the proposed systems, while they are also compared for their efficiency, accuracy and comprehensibility, to those of an inductive machine learning approach as well as to those of a standard genetic programming symbolic expression approach.Conclusion: The porposed GP-based intelligent methodologies are able to produce accurate and comprehensible results for medical experts performing competitive to other intelligent approaches. The aim of the authors was the production of accurate but also sensible decision rules that could potentially help medical doctors to extract conclusions, even at the expense of a higher classification score achievement