Letter to the Editor: Complementary statistical approaches for interpreting machine learning feature importance in osteoporosis risk.

This paper comments on the valuable contribution by Carvalho and Gavaia regarding machine learning for osteoporosis risk prediction, particularly their use of a stacking ensemble model and feature importance analysis. While acknowledging the model's high predictive accuracy, we raise a crucial concern: high accuracy does not inherently validate the reliability of feature importance interpretation. We discuss how the interpretation of feature importance from complex, model-dependent methods like those used can be influenced by model structure and data characteristics, potentially overemphasizing certain variables or reflecting model-specific relevance rather than true underlying causal drivers of osteoporosis risk. Validating feature importance is inherently difficult due to the absence of ground truth for causal relationships. To address these limitations and move beyond purely model-dependent predictive importance, we propose integrating complementary statistical methodologies, such as Spearman's rho, Kendall's tau, Mutual Information, and Total Correlation. These impartial and resilient methods can offer more robust insights into variable relationships. By combining predictive ML modeling with these statistical approaches, we aim to advance the understanding of complex health outcomes like osteoporosis in biomedical and healthcare applications, providing a more dependable assessment of feature importance and model behavior.