Machine learning for medical diagnosis: history, state of the art and perspective

The paper provides an overview of the development of intelligent data analy sis in medicine from a machine learning perspective: a historical view, a s tate-of-the-art view, and a view on some future trends in this subfield of applied artificial intelligence. The paper is not intended to provide a com prehensive overview but rather describes some subareas and directions which from my personal point of view seem to be important for applying machine l earning in medical diagnosis. In the historical overview, I emphasize the n aive Bayesian classifier, neural networks and decision trees. I present a c omparison of some state-of-the-art systems, representatives from each branc h of machine learning, when applied to several medical diagnostic tasks. Th e future trends are illustrated by two case studies. The first describes a recently developed method for dealing with reliability of decisions of clas sifiers, which seems to be promising for intelligent data analysis in medic ine. The second describes an approach to using machine learning in order to verify some unexplained phenomena from complementary medicine, which is no t (yet) approved by the orthodox medical community but could in the future play an important role in overall medical diagnosis and treatment. (C) 2001 Elsevier Science B.V. All rights reserved.