Robust multivariate methods in laboratory techniques and in assisting medical diagnosis

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Abstract
The usefulness of robust multivariate methods in medical applications, for example, the indirect estimation of total lung capacity by robust regression and the assistance of medical diagnosis in obstructive airways disease using robust discriminant functions, is discussed. The results of robust methods that consist in downweighting the influence of the multivariate outliers are compared with the outcomes of classical procedures. The advantages of modern robust algorithms are proved in the present study. It is planned to include the methods in the system for the computerized consulting unit for respiratory diseases that is being set up in Wroclaw.