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Article

Predictive Ability of Machine-Learning Methods for Vitamin D Deficiency Prediction by Anthropometric Parameters

1
Department of Statistics, University of Salamanca, 37007 Salamanca, Spain
2
Primary Care Research Unit of Salamanca (APISAL), Biomedical Research Institute of Salamanca (IBSAL), 37005 Salamanca, Spain
3
Health Service of Castilla and Leon (SACyL), 37005 Salamanca, Spain
4
Department of Medicine, University of Salamanca, 37007 Salamanca, Spain
5
Department of Biomedical and Diagnostic Sciences, University of Salamanca, 37007 Salamanca, Spain
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Carmen Lacave, Ana Isabel Molina and Florin Leon
Mathematics 2022, 10(4), 616; https://doi.org/10.3390/math10040616
Received: 30 November 2021 / Revised: 13 February 2022 / Accepted: 14 February 2022 / Published: 17 February 2022
Background: Vitamin D deficiency affects the general population and is very common among elderly Europeans. This study compared different supervised learning algorithms in a cohort of Spanish individuals aged 35–75 years to predict which anthropometric parameter was most strongly associated with vitamin D deficiency. Methods: A total of 501 participants were recruited by simple random sampling with replacement (reference population: 43,946). The analyzed anthropometric parameters were waist circumference (WC), body mass index (BMI), waist-to-height ratio (WHtR), body roundness index (BRI), visceral adiposity index (VAI), and the Clinical University of Navarra body adiposity estimator (CUN-BAE) for body fat percentage. Results: All the anthropometric indices were associated, in males, with vitamin D deficiency (p < 0.01 for the entire sample) after controlling for possible confounding factors, except for CUN-BAE, which was the only parameter that showed a correlation in females. Conclusions: The capacity of anthropometric parameters to predict vitamin D deficiency differed according to sex; thus, WC, BMI, WHtR, VAI, and BRI were most useful for prediction in males, while CUN-BAE was more useful in females. The naïve Bayes approach for machine learning showed the best area under the curve with WC, BMI, WHtR, and BRI, while the logistic regression model did so in VAI and CUN-BAE. View Full-Text
Keywords: vitamin D; machine learning; decision making; anthropometric parameters vitamin D; machine learning; decision making; anthropometric parameters
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MDPI and ACS Style

Patino-Alonso, C.; Gómez-Sánchez, M.; Gómez-Sánchez, L.; Sánchez Salgado, B.; Rodríguez-Sánchez, E.; García-Ortiz, L.; Gómez-Marcos, M.A. Predictive Ability of Machine-Learning Methods for Vitamin D Deficiency Prediction by Anthropometric Parameters. Mathematics 2022, 10, 616. https://doi.org/10.3390/math10040616

AMA Style

Patino-Alonso C, Gómez-Sánchez M, Gómez-Sánchez L, Sánchez Salgado B, Rodríguez-Sánchez E, García-Ortiz L, Gómez-Marcos MA. Predictive Ability of Machine-Learning Methods for Vitamin D Deficiency Prediction by Anthropometric Parameters. Mathematics. 2022; 10(4):616. https://doi.org/10.3390/math10040616

Chicago/Turabian Style

Patino-Alonso, Carmen, Marta Gómez-Sánchez, Leticia Gómez-Sánchez, Benigna Sánchez Salgado, Emiliano Rodríguez-Sánchez, Luis García-Ortiz, and Manuel A. Gómez-Marcos. 2022. "Predictive Ability of Machine-Learning Methods for Vitamin D Deficiency Prediction by Anthropometric Parameters" Mathematics 10, no. 4: 616. https://doi.org/10.3390/math10040616

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