Interpretable Diagnostics with SHAP-Rule: Fuzzy Linguistic Explanations from SHAP Values

Alexandra I. Khalyasmaa, Pavel V. Matrenin () and Stanislav A. Eroshenko
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Alexandra I. Khalyasmaa: Ural Power Engineering Institute, Ural Federal University Named After the First President of Russia B.N. Yeltsin, Ekaterinburg 620062, Russia
Pavel V. Matrenin: Ural Power Engineering Institute, Ural Federal University Named After the First President of Russia B.N. Yeltsin, Ekaterinburg 620062, Russia
Stanislav A. Eroshenko: Ural Power Engineering Institute, Ural Federal University Named After the First President of Russia B.N. Yeltsin, Ekaterinburg 620062, Russia

Mathematics, 2025, vol. 13, issue 20, 1-20

Abstract: This study introduces SHAP-Rule, a novel explainable artificial intelligence method that integrates Shapley additive explanations with fuzzy logic to automatically generate interpretable linguistic IF-THEN rules for diagnostic tasks. Unlike purely numeric SHAP vectors, which are difficult for decision-makers to interpret, SHAP-Rule translates feature attributions into concise explanations that humans can understand. The method was rigorously evaluated and compared with baseline SHAP and AnchorTabular explanations across three distinct and representative datasets: the CWRU Bearing dataset for industrial predictive maintenance, a dataset for failure analysis in power transformers, and the medical Pima Indians Diabetes dataset. Experimental results demonstrated that SHAP-Rule consistently provided clearer and more easily comprehensible explanations, achieving high expert ratings for simplicity and understanding. Additionally, SHAP-Rule exhibited superior computational efficiency and robust consistency compared to alternative methods, making it particularly suitable for real-time diagnostic applications. Although SHAP-Rule showed minor trade-offs in coverage, it maintained high global fidelity, often approaching 100%. These findings highlight the significant practical advantages of linguistic fuzzy explanations generated by SHAP-Rule, emphasizing its strong potential for enhancing interpretability, efficiency, and reliability in diagnostic decision-support systems.

Keywords: explainable artificial intelligence; Shapley additive explanations; fuzzy rules; linguistic variables; diagnostics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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