Multivariate Approach to Predictive Diagnosis of Bone-marrow Iron Stores

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Abstract
Beck, J. Robert, Cornwell, Gibbons G., III, and Rawnsley, Howard M.: Multivariate approach to predictive diagnosis of bone-marrow iron stores. Am J Clin Pathol 70:665–670, 1978. The authors constructed a computer-assisted multivariate data analysis system that finds the subset of a group of laboratory tests (i.e., experimental variables) best able to discriminate between previously identified subpopulations. A Bayesian decision-analysis algorithm was devised for predictive diagnosis by the use of sample means and standard deviations of those identified tests. This approach was used on a set of nine hematologic laboratory tests to find a subset capable of efficiently predicting the presence or absence of bone-marrow iron stores. This identified subset proved to have a diagnostic efficiency of 0.90. The success of this system suggests the feasibility of such an approach in finding a “best” subset of loosely related variables.

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