Abstract
Nutritional assessment is an important evaluation to prevent and control malnutrition, which is one of the main causes associated with child mortality. Weight and height are the most frequently measured morphological traits which in combination with child's gender and age, generates anthropometric indices to establish child's nutritional status. Nevertheless, accomplishment of this task in rural areas is difficult because of complications to transport bulky and heavy equipment, which must be properly and adequately calibrated. This work proposed a novel approach to perform nutritional assessments of children under five, through a system focused on the estimation of anthropometric indices, based on the measurements obtained from a set of body part images and its relations with child's gender and age. The results showed that sensitivity and specificity for the anthropometric indicators, ranged from 66% to 100% and 88% to 100%, respectively. Moreover, overall accuracies were over 85% up to 100%. Additionally, the experiments conducted shown our method as a viable solution to perform nutritional evaluations via accurate anthropometric index estimations.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2016 IEEE ANDESCON, ANDESCON 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781509025312 |
| DOIs | |
| State | Published - 27 Jan 2017 |
| Externally published | Yes |
| Event | 2016 IEEE ANDESCON, ANDESCON 2016 - Arequipa, Peru Duration: 19 Oct 2016 → 21 Oct 2016 |
Publication series
| Name | Proceedings of the 2016 IEEE ANDESCON, ANDESCON 2016 |
|---|
Conference
| Conference | 2016 IEEE ANDESCON, ANDESCON 2016 |
|---|---|
| Country/Territory | Peru |
| City | Arequipa |
| Period | 19/10/16 → 21/10/16 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 2 Zero Hunger
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SDG 3 Good Health and Well-being
Keywords
- image processing
- machine learning
- malnutrition
- neural networks
- nutrition assessment
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