N 5 (190) 2024. P. 74–78

DEPENDENCE OF VITAMIN D LEVEL ON LABORATORY AND ANTHROPOMETRIC INDICATORS: APPLICATION OF MACHINE LEARNING METHODS FOR SCREENING IN ADULTS

V. V. Babienko, A. V. Shanyhin, A. M. Rozhnova, Ye. M. Strakhov, A. S. Korkhova

Odesa National Medical University, Odesa, Ukraine
Odesa I. I. Mechnikov National University, Odesa, Ukraine

DOI 10.32782/2226-2008-2024-5-12

Vitamin D deficiency is now recognized as an international health issue, affecting a variety of physiological systems and disease outcomes.

Purpose. The present study proposes machine learning models to identify individuals at risk of vitamin D deficiency.

Materials and methods. Machine learning was used on the dataset of 944 persons’ laboratory analysis to determine the list of anthropometric and laboratory indicators that affect the development of vitamin D deficiency. It was built a decision tree with a depth of 5 to predict vitamin D deficiency based on various parameters.

Results. The authors found feature importance in identifying potential vitamin D deficiency. Age and BMI were considered the most impactful anthropometric parameters, level of HDL was the most important laboratory parameter. A heatmap matrix for correlation of features between one another was created. It was calculated metrics based on the confusion matrix for determining the risk of a 25(OH)D deficit: Accuracy, Precision, Sensitivity, Specificity, F1-Score. The authors plotted the ROC curve of the optimal model; established that the Area Under the Curve (AUC) of the selected model is equal to 0.92 that is a very effective result.

Conclusion. Machine learning techniques are more effective at predicting deficiencies than traditional statistical methods.

Key words: vitamin D, prevention, lipid metabolism, anthropometry, artificial intelligence, machine learning.

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