An unsupervised probabilistic net (UPN) is introduced to identify health inequalities among countries according to their health status measured by the collected health indicators. By estimating the underlying probability density function of the health indicators using UPN, countries, which have similar health status, will be categorized into the same cluster. From this, the intercluster health inequalities are identified by the Mahalanobis distance, and the intracluster health inequalities are identified by the diversity within the clusters. To extract the typical health status, the concept of virtual objects is used in this study. Each virtual object in this study, therefore, represents a hypothetical country, which does not exist in a data set but can be found through learning. The identified virtual objects represent the hidden knowledge in a data set and can be valuable to social scientists in health promotion planning. Moreover, the investigation of the behavior of the virtual objects can help us to find the realistic and reasonable health promotion target for a country with a poor health status.
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