Machine Learning Algorithm for Early Detection of Deficiency of Vitamin D

This study explores the use of machine learning algorithms for the early identification of Vitamin D deficiency, a prevalent global health concern. Utilizing a range of supervised learning methods, including logistic regression, decision trees, and support vector machines, the research investigates demographic, clinical, and lifestyle factors as predictors. The dataset incorporates anthropometric measurements, medical history, and various other health indicators. The findings demonstrate that machine learning models can accurately predict Vitamin D status, with high sensitivity and specificity, enabling early screening and diagnosis. The study emphasizes the potential of AI in preventive healthcare and suggests future research directions for integrating these models into therapeutic processes.