Pattern recognition in medicine--a place for the use of principal component analysis and factor analysis.

Abstract

Summary: Pattern recognition, which depends upon the perception of inter-relationships between separate observations, has a central role in medical science. Principal component analysis and factor analysis are statistical techniques which can define structural patterns within a set of observations and assign appropriate weights to the importance of each observed variable in contributing to each part of the pattern. The technique of principal component analysis is exemplified by the definition of a “disease activity index” which has been used in the assessment of response of systemic lupus erythematosus to immunosuppressive therapy. Factor analysis of responses to a computer-based questionnaire (SASH) has been used to define patterns of symptoms which correspond to gastrointestinal and cardiac syndromes. Both techniques allow a quantitative approach to the recognition of patterns of disease and should be more widely used in medicine.