The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
There are many examples in the literature of scorecards derived from clinical data. These scorecards are proposed for use by health professionals to stratify patients into risk categories and are often compared using receiver operating characteristic (ROC) curves and their associated areas (AUC). This paper analyses random scorecards and shows that the underlying distributions and therefore statistical significance of the AUC metric is dependent on both the dimensionality of the scorecard and the distribution of classes in the data. This finding suggests that scorecards of differing dimensionality and distribution should not be compared directly on AuC values and that smaller scorecards would be expected to have a lower mean solely by chance.