Explainable Machine Learning in Healthcare: Methods, Interpretation, and Applications for Clinical Research

A recent arXiv research paper details a structured review of Explainable Machine Learning (XML) methodologies, including SHAP, LIME, PDP, and ICE plots, for applications in healthcare. The paper explains the mechanisms of these tools, visualizes their outputs, and provides guidance on interpretation, appropriate use, and limitations. These techniques offer visual and quantitative insights into how predictors influence model predictions, demonstrated using a publicly available Heart Disease dataset.

Why it matters

This research is strategically important because it addresses the critical need for transparency and interpretability in machine learning models, particularly within sensitive domains like healthcare. Understanding how AI models arrive at their conclusions is crucial for building trust, ensuring accountability, and facilitating wider adoption of these technologies, which can directly impact decision-making processes.

Key insights

• The research provides a structured review of common Explainable Machine Learning (XML) methods relevant to healthcare.
• Key XML methodologies discussed include SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), Partial Dependence Plots (PDP), and Individual Conditional Expectation (ICE) plots.
• The paper explains the underlying mechanisms of each XML method at a high level.
• Visualizations of representative outputs are provided for each XML method.
• Guidance is offered on the interpretation, appropriate use, and limitations of these XML techniques.
• The utility of these methods is illustrated using the publicly available Heart Disease dataset.
• XML techniques provide intuitive visual and quantitative insights into how predictors influence model predictions.
• Global methods characterize population-level feature impacts.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.07522