Determinants of Serum Vitamin D level; A Data Mining Approach

Document Type : Original Article


1 Department of Pure Mathematics, Center of Excellence in Analysis on Algebraic Structures (CEAAS), Ferdowsi University of Mashhad, P.O. Box 1159, Mashhad 91775, Iran.

2 International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran.

3 Department of Nutrition, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.

4 Student Research Committee, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.

5 Norwegian Center for E-Health Research, University Hospital of North Norway, Tromsø, Norway.

6 Department of Biology, Faculty of Sciences, Mashhad Branch, Islamic Azad University, Mashhad, Iran.

7 Division of Medical Education, Brighton & Sussex Medical School, Falmer, Brighton, Sussex, UK.

8 Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.


Introduction:  Serum vitamin D levels are related to a wide spectrum of factors including low sunlight exposure, high oxidative stress, low physical activity and sleep disorders. In this paper we are going to investigate the most crucial parameters associated with serum vitamin D levels in survey of ultraviolet intake by nutritional approach (SUVINA) study with a data mining approach.
Material and Methods: Data including demographic, anthropometric, clinical and laboratory information were extracted from the SUVINA dataset comprising 289 subjects who were enrolled into our study. The XGBoost algorithm was used to define the most important features related to vitamin D level in our population.
Results: Applying XGBoost modeling for vitamin D level showed that the presented scheme can determine the most important determinants of serum vitamin D level with an accuracy of 91%. Pro-oxidant anti-oxidant balance (PAB), body fat percentage, physical activity level (PAL), age, restless leg syndrome (RLS), and dietary inflammatory index (DII) density were the most important variables correlated with vitamin D deficiency.
Conclusion: Using XGBoost and with an accuracy of more than 90%, we showed that the six most important risk factors for vitamin D deficiency are PAB, PAL, age, body fat percentage, RLS and DII density, respectively.


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