Abstract
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 duster. 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.
| Original language | English |
|---|---|
| Pages (from-to) | 46-57 |
| Number of pages | 12 |
| Journal | IEEE Transactions on Neural Networks |
| Volume | 14 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jan 2003 |
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Keywords
- Clustering
- Health inequalities
- Health promotion
- Probabilistic net
- Virtual objects
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An unsupervised probabilistic net for health inequalities analysis. / Yang, Zheng Rong; Harrison, Robert G.
In: IEEE Transactions on Neural Networks, Vol. 14, No. 1, 01.2003, p. 46-57.Research output: Contribution to journal › Article
TY - JOUR
T1 - An unsupervised probabilistic net for health inequalities analysis
AU - Yang, Zheng Rong
AU - Harrison, Robert G.
PY - 2003/1
Y1 - 2003/1
N2 - 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 duster. 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.
AB - 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 duster. 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.
KW - Clustering
KW - Health inequalities
KW - Health promotion
KW - Probabilistic net
KW - Virtual objects
UR - http://www.scopus.com/inward/record.url?scp=0037274396&partnerID=8YFLogxK
U2 - 10.1109/TNN.2002.806956
DO - 10.1109/TNN.2002.806956
M3 - Article
VL - 14
SP - 46
EP - 57
JO - IEEE Transactions on Neural Networks
JF - IEEE Transactions on Neural Networks
SN - 1045-9227
IS - 1
ER -