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Abstract
Currently used body surface area (BSA) formulas give satisfactory results only for individuals with typical physique, while for elderly, obese or anorectic people accurate results cannot be expected. Particularly noteworthy are the results for individuals with severe obesity (body-mass index greater than 35 kg/m2), for which BSA estimation errors reached 80%. The main goal of our study is the development of precise BSA models for specific body parts. We have achieved satisfactory results for a wide range of patients. Using regression models, such as: support vector regression, multilayer perceptron regressor, stochastic gradient descent, or ridge regression, a fourfold decrease in errors proportion is achieved. Machine learning algorithms led to reduction from 1.2 to 8 times for mean estimation error.
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- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Title of issue:
- 2018 International Interdisciplinary PhD Workshop (IIPhDW) strony 316 - 318
- Language:
- English
- Publication year:
- 2018
- Bibliographic description:
- RZYMAN G., Redlarski G., Pałkowski A., Tojza P., Krawczuk M., Siebert J.: Computing methods for fast and precise body surface area estimation of selected body parts// 2018 International Interdisciplinary PhD Workshop (IIPhDW)/ : , 2018, s.316-318
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/iiphdw.2018.8388380
- Sources of funding:
-
- „Metodyka szybkiego oraz dokładnego wyznaczania powierzchni ciała człowieka” — projekt realizowany w ramach grantu programu OPUS Narodowego Centrum Nauki (2014/15/B/NZ7/01018)
- Verified by:
- Gdańsk University of Technology
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