A number of classification techniques have been applied to the analysis of medical diagnostic systems and decision making. Commonly used approaches such as cluster analysis, linear discriminant analysis and Bayesian classification are subject to logical and statistical limitations. In this paper we present a methodology, called »grade of membership« analysis, which resolves many of those limitations. This methodology deals simultaneously with the dual problems of case clustering and estimation of discriminant coefficients. The methodology also permits the assessment of the reliability of externally defined medical diagnoses, multiple diagnoses for individuals, disease progression and severity, and permits the representation of patient heterogeneity within diagnostic category. Maximum likelihood principles are invoked both to obtain parameter estimates and as a basis for likelihood ratio testing of complex hypotheses about the model structure. The model is illustrated by an analysis of data on abdominal symptoms and disease.
Tensometric tremorography in high-precision medical diagnostic systems
