Early Identification of Vitamin D Deficiency Risk Through Public Health Screening Data

Metabolic syndrome, characterized by central obesity, hypertension, hyperglycemia, dyslipidemia, and reduced high-density lipoprotein levels, significantly increases the risk of cardiovascular diseases. Vitamin D, essential for calcium regulation and immune modulation, has been linked to reduced inflammation and metabolic syndrome. This study aimed to develop machine learning models to predict vitamin D deficiency using data from publicly funded health check-ups in Taiwan. A total of 6,046 adults aged 30 years and older were included, with data on demographics, anthropometric measures, and biochemical indicators. Six algorithms, including logistic regression, random forest, SVM, XGBoost, LightGBM, and MLP, were evaluated. Models were trained and tuned using stratified sampling and K-fold cross-validation. XGBoost demonstrated the best overall performance, with high accuracy, F1-Score, and balanced precision and recall, supporting its applicability for predicting vitamin D deficiency. Future research should address feature quality, class imbalance, and dataset diversity to enhance predictive frameworks for vitamin D deficiency.