Resolving Interpretation Challenges in Machine Learning Feature Selection With an Iterative Approach in Biomedical Pain Data

This study presents an iterative machine learning framework that improves the identification of trait-relevant features in biomedical pain data. This framework reduces ambiguity in feature selection and clarifies interpretation, helping to distinguish robust, meaningful predictors from coincidental ones. This approach enhances the interpretation and transparency of machine learning analyses in pain research and related biomedical fields.