| Autor: |
Nicolescu, Alina, Dolenko, Brion, Bezabeh, Tedros, Stefan, Lorena-Ivona, Ciurtin, Coziana, Kovacs, Eugenia, Smith, Ian C. P., Simionescu, Bogdan C., Deleanu, Calin |
| Jazyk: |
angličtina |
| Rok vydání: |
2013 |
| Předmět: |
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| Popis: |
A NMR dataset with non-buffered urine samples consisting of 73 controls and 94 type II diabetes was suojeci to an in-house statistical classifier. A model was developed based on two glucose-free regions of the spectrum and those maximally discriminatory subregions selected most often by the algorithm were noted. The final classifier achieved 83.0% sensitivity and 83.6% specificity, with 83.2% overall accuracy. There were five spectral subregions selected by the algorithm as most relevant for discrimination. The protocol works well with non-buffered samples and has the potential for an automated clinical diagnosis of diabetes. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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