Maximizing Overall Percentage Accuracy in Classification: Discriminating Study Groups in the National Pressure Ulcer Long-Term Care Study (NPULS)

Paul R. Yarnold

Optimal Data Analysis, LLC

UniODA may be used to identify two different types of (weighted) maximum-accuracy models. First, ODA can identify models that explicitly maximize overall percentage accuracy in classification or PAC—that is, the percentage of the total sample that is correctly classified by the model. Second, ODA can identify models that explicitly maximize the predictive accuracy of the model normed against chance using the effect strength for sensitivity (ESS) statistic, that is both chance-corrected (0 = the predictive accuracy expected by chance for the application) and maximum-corrected (100 = perfect, errorless classification). Because comparatively little is known about optimal models that maximize PAC, this research note initiates a literature on the matter. The present exposition involves assessing if clinical and demographic factors can be discriminated on the basis of study group using a UniODA model that explicitly maximizes PAC.

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