Developing a Model for Prediction of Serum 25-Hydroxyvitamin D Level: The Use of Linear Regression and Machine Learning Methods

Abstract

Objective Because vitamin D status affects many organs and tissues of the body, it is important to determine the factors affecting it. The purpose of this study was to develop a model for predicting the serum 25-hydroxyvitamin D [25(OH)D] level in healthy young adults. Method This cross-sectional study was conducted on 201 healthy individuals aged 20 to 40 years old in Shiraz, Iran. Data regarding demographic characteristics, vitamin D intake through supplements, and sun exposure habits were gathered. Serum 25(OH)D concentration was also measured. Data were analyzed with R software using linear regression and different machine learning methods such as conditional tree, conditional forest and random forest. Results Based on the linear regression, male sex (p < 0.001), taking 50,000 IU vitamin D3 supplement monthly (p < 0.001), and lower waist circumference (p = 0.018) were identified as effective factors in increasing serum 25(OH)D levels. According to the conditional tree, taking 50,000 IU vitamin D3 supplement monthly (p < 0.001) and sex (p < 0.001) were two main factors in the classification of individuals in terms of serum 25(OH)D levels. Besides, conditional forest and random forest results showed that the most important variable was taking 50,000 IU vitamin D3 supplement monthly. Conclusions Supplement use is the first and most important predictor of 25(OH)D levels and other factors, including sex and waist circumference, are ranked thereafter, and the importance of these factors is greater in those who do not take vitamin D3 supplements.