Non-Invasive Prediction of Vitamin-D Status by Using Machine Learning Algorithm

Vitamin D deficiency is a widespread health issue that can lead to various complications, including bone disorders, kidney diseases, malabsorption syndromes and immune dysfunctions. Traditional Vitamin D testing methods are invasive and costly, making it difficult to screen large populations efficiently. Prediction of individual's Vitamin D status based on their Body Mass Index (BMI) using machine learning algorithm, by developing a predictive model using BMI, a commonly measured health metric, we can estimate Vitamin D levels non-invasively and more cost-effectively. The results indicate that machine learning can effectively predict Vitamin D deficiency, which could be useful for healthcare providers in early screening and intervention strategies