Vitamin D Deficiency Detection: A Novel Ensemble Approach with Interpretability Insights

Vitamin D deficiency is becoming a global public health concern, particularly in medical centers, with serious consequences for disease severity, mortality, and morbidity. Traditional diagnostic methodologies struggle with cost and are time-consuming. This drives a paradigm change toward the use of automated process for improved predicted accuracy and cost-effectiveness in diagnosing Vitamin D deficiency. In addressing the global challenge of Vitamin D deficiency, this study introduces an ensemble model that synergistically combines LightGBM and CatBoost algorithms, marking a significant leap in diagnostic methodologies. By leveraging the strengths of these advanced machine learning techniques, our approach achieves an impressive 96% accuracy on the Vitamin D Deficiency (VDD) Dataset, demonstrating substantial improvement over traditional diagnostic methods. The integration of SnAP (Shapley Additive explanations) for interpretability further enhances the utility of our model, providing clear insights into the impact of individual features on the severity predictions of Vitamin D deficiency. This revised abstract concisely encapsulates the research objectives, innovative methodology, and critical findings, underscoring the potential to revolutionize diagnostic efficiency and accuracy in the medical domain.