Interpretable machine learning with tree-based shapley additive explanations: Application to metabolomics datasets for binary classification

Analysis

Why This Paper Matters

Machine learning is increasingly used in clinical metabolomics to discover biomarkers that discriminate between case and control groups. However, the interpretability of these models is crucial for understanding the underlying biomedical problem and for building trust in the discoveries. Traditional methods like PLS-DA offer global interpretability through VIP scores, but they may lack the depth needed to fully explain complex models. This paper introduces Tree-based SHAP, a game-theoretic approach, to provide both local and global explanations for ML models in metabolomics. By comparing SHAP with VIP scores, the authors highlight the need for more sophisticated interpretability tools as ML models grow in complexity. This work is timely and relevant for practitioners aiming to deploy interpretable ML in clinical research.

Technical Contributions

• Application of Tree SHAP to interpret random forest models in metabolomics binary classification.
• Comparative analysis of SHAP and PLS-DA VIP scores, demonstrating SHAP's superior explanation depth.
• Evaluation of multiple ML models (PLS-DA, random forests, gradient boosting, XGBoost) across three published metabolomics datasets.
• Emphasis on local explanation properties of SHAP, enabling per-sample interpretation.
• Practical demonstration of how SHAP can rationalize model predictions in biomedical contexts.

Results

The study found that SHAP provides more detailed and nuanced explanations than PLS-DA's VIP scores. While VIP scores offer a global ranking of variable importance, SHAP values reveal both the magnitude and direction of feature contributions for individual predictions, as well as global importance. This deeper explanation depth allows researchers to better understand why a model makes specific predictions, which is essential for biomarker validation. The results are qualitative, with no specific metrics reported, but the comparative analysis clearly favors SHAP for interpretability.

Significance

This paper contributes to the growing body of work on interpretable machine learning in healthcare and life sciences. By showcasing SHAP's advantages in metabolomics, it encourages researchers to adopt more transparent ML practices. The findings have implications beyond metabolomics, as SHAP can be applied to other omics data and clinical prediction tasks. Ultimately, improved interpretability can lead to greater confidence in ML-driven biomarker discoveries and facilitate their translation into clinical practice.