Geometric morphometrics approach for classifying children’s nutritional status on out of sample data
Abstract Current alignment-based methods for classification in geometric morphometrics do not generally address the classification of new individuals that were not part of the study sample. However, in the context of infant and child nutritional assessment from body shape images this is a relevant p...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85718-4 |
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| author | Medialdea Laura Arribas-Gil Ana Pérez-Romero Álvaro Gómez Amador |
| author_facet | Medialdea Laura Arribas-Gil Ana Pérez-Romero Álvaro Gómez Amador |
| author_sort | Medialdea Laura |
| collection | DOAJ |
| description | Abstract Current alignment-based methods for classification in geometric morphometrics do not generally address the classification of new individuals that were not part of the study sample. However, in the context of infant and child nutritional assessment from body shape images this is a relevant problem. In this setting, classification rules obtained on the shape space from a reference sample cannot be used on out-of-sample individuals in a straightforward way. Indeed, a series of sample dependent processing steps, such as alignment (Procrustes analysis, for instance) or allometric regression, need to be conducted before the classification rule can be applied. This work proposes ways of obtaining shape coordinates for a new individual and analyzes the effect of using different template configurations on the sample of study as target for registration of the out-of-sample raw coordinates. Understanding sample characteristics and collinearity among shape variables is crucial for optimal classification results when evaluating children’s nutritional status using arm shape analysis from photos. The SAM Photo Diagnosis App© Program’s goal is to develop an offline smartphone tool, enabling updates of the training sample across different nutritional screening campaigns. |
| format | Article |
| id | doaj-art-af558ca6cff74bf0b6ddfa4785bfb22e |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-af558ca6cff74bf0b6ddfa4785bfb22e2025-02-02T12:17:35ZengNature PortfolioScientific Reports2045-23222025-01-0115111410.1038/s41598-025-85718-4Geometric morphometrics approach for classifying children’s nutritional status on out of sample dataMedialdea Laura0Arribas-Gil Ana1Pérez-Romero Álvaro2Gómez Amador3Research, Development and Innovation Department, Action Against HungerStatistics Department, Universidad Carlos III de MadridStatistics Department, Universidad Carlos III de MadridResearch, Development and Innovation Department, Action Against HungerAbstract Current alignment-based methods for classification in geometric morphometrics do not generally address the classification of new individuals that were not part of the study sample. However, in the context of infant and child nutritional assessment from body shape images this is a relevant problem. In this setting, classification rules obtained on the shape space from a reference sample cannot be used on out-of-sample individuals in a straightforward way. Indeed, a series of sample dependent processing steps, such as alignment (Procrustes analysis, for instance) or allometric regression, need to be conducted before the classification rule can be applied. This work proposes ways of obtaining shape coordinates for a new individual and analyzes the effect of using different template configurations on the sample of study as target for registration of the out-of-sample raw coordinates. Understanding sample characteristics and collinearity among shape variables is crucial for optimal classification results when evaluating children’s nutritional status using arm shape analysis from photos. The SAM Photo Diagnosis App© Program’s goal is to develop an offline smartphone tool, enabling updates of the training sample across different nutritional screening campaigns.https://doi.org/10.1038/s41598-025-85718-4Generalized procustes analysisAllometryAcute malnutritionClassificationGeometric morphometrics |
| spellingShingle | Medialdea Laura Arribas-Gil Ana Pérez-Romero Álvaro Gómez Amador Geometric morphometrics approach for classifying children’s nutritional status on out of sample data Scientific Reports Generalized procustes analysis Allometry Acute malnutrition Classification Geometric morphometrics |
| title | Geometric morphometrics approach for classifying children’s nutritional status on out of sample data |
| title_full | Geometric morphometrics approach for classifying children’s nutritional status on out of sample data |
| title_fullStr | Geometric morphometrics approach for classifying children’s nutritional status on out of sample data |
| title_full_unstemmed | Geometric morphometrics approach for classifying children’s nutritional status on out of sample data |
| title_short | Geometric morphometrics approach for classifying children’s nutritional status on out of sample data |
| title_sort | geometric morphometrics approach for classifying children s nutritional status on out of sample data |
| topic | Generalized procustes analysis Allometry Acute malnutrition Classification Geometric morphometrics |
| url | https://doi.org/10.1038/s41598-025-85718-4 |
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