Abstract
One of the threatening trends of health to the youth in recent years has been the metabolic syndrome. Many associate this syndrome to how big the fatty tissue around the belly is. Self-organizing maps (SOM) can be viewed as a visualization tool that projects high-dimensional dataset onto a two-dimensional plane making the complexity of the data be simplified and in the process disclose much of the hidden details for easy analyzes, clustering and visualization. This paper focuses on the analysis, visualization and prediction of the syndrome trends using both spherical and Torus SOM with a view to diagnose its trends, inter-relate other risk factors as well as evaluating the responses obtained from the two approaches of SOM.
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Kihato, P.K. et al. (2008). Spherical and Torus SOM Approaches to Metabolic Syndrome Evaluation. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4985. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69162-4_29
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DOI: https://doi.org/10.1007/978-3-540-69162-4_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69159-4
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