Determination of Factors Affecting Vitamin D Levels in Women at Risk Using Classification and Regression Tree Analysis

Antoaneta Yordanova; Anna  Tolekova; Pavlina Teneva; Ivelina  Dobreva; Katya Kichukova

doi:10.3889/oamjms.2023.11340

Authors

Antoaneta Yordanova Department of Social Medicine, Healthcare Management and Disaster Medicine, Trakia University, Stara Zagora, Bulgaria https://orcid.org/0000-0002-9901-8504
Anna Tolekova Department of Health Care, Medical College, Trakia University, Stara Zagora, Bulgaria https://orcid.org/0000-0002-1692-0739
Pavlina Teneva Department of Health Care, Medical College, Trakia University, Stara Zagora, Bulgaria https://orcid.org/0000-0002-2184-2114
Ivelina Dobreva Department of Health Care, Medical College, Trakia University, Stara Zagora, Bulgaria https://orcid.org/0000-0003-0999-7831
Katya Kichukova Department of Health Care, Medical College, Trakia University, Stara Zagora, Bulgaria https://orcid.org/0000-0001-7141-3433

DOI:

https://doi.org/10.3889/oamjms.2023.11340

Keywords:

Vitamin D, Premenopausal and menopausal women, Indoor working women health, Classification and regression trees, Data mining

Abstract

BACKGROUND: The influence of the factors on Vitamin D as a health indicator in premenopausal and menopausal women is a significant subject to be investigated.

AIM: The study uses the potential of classification and regression trees (CART) as a data mining method for medical type samples.

METHODS: The data set is built by records of 84 indoor working women at the age of 45 to 67 years from five Bulgarian companies. The data are obtained through laboratory tests of serum concentrations of 25-OH-Vitamin D and a questionnaire, created for the study. Statistical data processing is made by descriptive statistics and the CART method.

RESULTS: The results show Vitamin D deficiency in 69% of the studied women at risk from Stara Zagora. For the target variable – Vitamin D (the quantity of 25-OH-Vitamin D), a regression CART tree was built. The calculated percentages of normalized importance for each independent variable reveal that the most important predictors, affecting Vitamin D, are body mass index (100%), alcohol (84.2%), education (70.3%), coffee (70.2%), Са_Vit D (69.8%), and sports frequency (42.4%), while the other variables have much less importance.

CONCLUSION: The application of the CART method makes it possible to study the distribution and importance of the factors influencing the state of vitamin D. The presence of such a high percentage of women at risk requires a comprehensive approach, including educational programs and strict application of guidelines for vitamin D supplementation to prevent the effects of hypovitaminosis.

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