Medical Diagnosis with Non-Parametric Allocation Rules
In experiments with automatic diagnosis, parametric models have been employed very often even when the presuppositions were lacking and prognoses concerning the expected error rate were sometimes established on the basis of the model presuppositions. The prognoses very often proved to be false (i.e. too favourable): one reason for this optimistic bias lies in the fact that the prognoses are given mostly without verifying whether the presuppositions are fulfilled for the problem at hand. As a result of this loose management with mathematical models, the automatic diagnostic-aid has fallen into discredit. As there is usually little information concerning the distribution of the variables as regards medical problems, the non- parametric case assumes a special meaning for medical application and particularly for diagnostic problems on the basis of random vectors with unknown distribution function. The suitable mathematical model for this situation is to be sought in the class of non-parametric allocation rules.
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