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

Author

Listed:
  • Carmen Patino-Alonso

    (Department of Statistics, University of Salamanca, 37007 Salamanca, Spain
    Primary Care Research Unit of Salamanca (APISAL), Biomedical Research Institute of Salamanca (IBSAL), 37005 Salamanca, Spain)

  • Marta Gómez-Sánchez

    (Primary Care Research Unit of Salamanca (APISAL), Biomedical Research Institute of Salamanca (IBSAL), 37005 Salamanca, Spain)

  • Leticia Gómez-Sánchez

    (Primary Care Research Unit of Salamanca (APISAL), Biomedical Research Institute of Salamanca (IBSAL), 37005 Salamanca, Spain)

  • Benigna Sánchez Salgado

    (Primary Care Research Unit of Salamanca (APISAL), Biomedical Research Institute of Salamanca (IBSAL), 37005 Salamanca, Spain
    Health Service of Castilla and Leon (SACyL), 37005 Salamanca, Spain)

  • Emiliano Rodríguez-Sánchez

    (Primary Care Research Unit of Salamanca (APISAL), Biomedical Research Institute of Salamanca (IBSAL), 37005 Salamanca, Spain
    Health Service of Castilla and Leon (SACyL), 37005 Salamanca, Spain
    Department of Medicine, University of Salamanca, 37007 Salamanca, Spain)

  • Luis García-Ortiz

    (Primary Care Research Unit of Salamanca (APISAL), Biomedical Research Institute of Salamanca (IBSAL), 37005 Salamanca, Spain
    Health Service of Castilla and Leon (SACyL), 37005 Salamanca, Spain
    Department of Biomedical and Diagnostic Sciences, University of Salamanca, 37007 Salamanca, Spain
    These authors contributed equally to this work.)

  • Manuel A. Gómez-Marcos

    (Primary Care Research Unit of Salamanca (APISAL), Biomedical Research Institute of Salamanca (IBSAL), 37005 Salamanca, Spain
    Health Service of Castilla and Leon (SACyL), 37005 Salamanca, Spain
    Department of Medicine, University of Salamanca, 37007 Salamanca, Spain
    These authors contributed equally to this work.)

Abstract

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.

Suggested Citation

  • Carmen Patino-Alonso & Marta Gómez-Sánchez & Leticia Gómez-Sánchez & Benigna Sánchez Salgado & Emiliano Rodríguez-Sánchez & Luis García-Ortiz & Manuel A. Gómez-Marcos, 2022. "Predictive Ability of Machine-Learning Methods for Vitamin D Deficiency Prediction by Anthropometric Parameters," Mathematics, MDPI, vol. 10(4), pages 1-16, February.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:4:p:616-:d:751374
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    References listed on IDEAS

    as
    1. Maher Maalouf, 2011. "Logistic regression in data analysis: an overview," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 3(3), pages 281-299.
    2. Shuyu Guo & Robyn M Lucas & Anne-Louise Ponsonby & the Ausimmune Investigator Group, 2013. "A Novel Approach for Prediction of Vitamin D Status Using Support Vector Regression," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-9, November.
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    Cited by:

    1. Carmen Lacave & Ana Isabel Molina, 2023. "Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education," Mathematics, MDPI, vol. 11(6), pages 1-4, March.

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