Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
Tafeit, E; Möller, R; Sudi, K; Reibnegger, G.
The determination of three subcutaneous adipose tissue compartments in non-insulin-dependent diabetes mellitus women with artificial neural networks and factor analysis.
Artif Intell Med. 1999; 17(2):181-193
Doi: 10.1016%2FS0933-3657%2899%2900017-2
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- Führende Autor*innen der Med Uni Graz
- Tafeit Erwin
- Co-Autor*innen der Med Uni Graz
- Möller Reinhard
- Reibnegger Gilbert
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- Abstract:
- The optical device LIPOMETER allows for non-invasive, quick, precise and safe determination of subcutaneous fat distribution, so-called subcutaneous adipose tissue topography (SAT-Top). In this paper, we show how the high-dimensional SAT-Top information of women with type-2 diabetes mellitus (non-insulin-dependent diabetes mellitus (NIDDM)) and a healthy control group can be analysed and represented in low-dimensional plots by applying factor analysis and special artificial neural networks. Three top-down sorted subcutaneous adipose tissue compartments are determined (upper trunk, lower trunk, legs). NIDDM women provide significantly higher upper trunk obesity and significantly lower leg obesity ('apple' type), as compared with their healthy control group. Further, we show that the results of the applied networks are very similar to the results of factor analysis.
- Find related publications in this database (using NLM MeSH Indexing)
- Adipose Tissue - pathology
- Aged - pathology
- Body Composition - physiology
- Diabetes Mellitus, Type 2 - pathology
- Factor Analysis, Statistical - pathology
- Female - pathology
- Humans - pathology
- Middle Aged - pathology
- Neural Networks (Computer) - pathology
- Find related publications in this database (Keywords)
- Neural Networks
- Pattern Recognition
- Factor Analysis
- Subcutaneous Adipose Tissue Topography (Sat-Top)
- Lipometer
- NIDDM