Explainable Machine Learning in Healthcare: Methods, Interpretation, and Applications for Clinical Research
We present a structured review of commonly used Explainable machine learning (XML) methodologies, including global and local interpretability tools such as SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), Partial Dependence Plots (PDP), and Individual Conditional Expectation (ICE) plots. For each method, we explain the underlying mechanism at a high level, visualize representative outputs, and provide structured guidance on interpretation, appropriate use, and limitations, illustrated using the publicly available Heart Disease dataset. XML techniques provided intuitive visual and quantitative insights into how predictors influence model predict...
| Authors | Krishna Padmanabhan, Minxin Lu, Dai Feng et al. |
|---|---|
| Primary category | cs.CY |
| Published | 13 July 2026 |
| Last updated at source | 13 July 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.