# Intelligent System Using Data to Support Decision-Making
###### Abstract  
Interest in explainable machine learning has grown, particularly in healthcare, where transparency and trust are essential. We developed a semi-automated evaluation framework within a clinical decision support system (CDSS-EQCM) that integrates LIME and SHAP explanations with multi-criteria decision-making (TOPSIS and Borda count) to rank model interpretability. After two-phase preprocessing of 2934 COVID-19 patient records spanning four epidemic waves, we applied five classifiers (Random Forest, Decision Tree, Logistic Regression, k-NN, SVM). Five infectious disease physicians used a Streamlit interface to generate patient-specific explanations and rate models on accuracy, separability, stability, response time, understandability, and user experience. Random Forest combined with SHAP consistently achieved the highest rankings in Borda count. Clinicians reported reduced evaluation time, enhanced explanation clarity, and increased confidence in model outputs. These results demonstrate that CDSS-EQCM can effectively streamline interpretability assessment and support clinician decision-making in medical diagnostics. Future work will focus on deeper electronic medical record integration and interactive parameter tuning to further enhance real-time diagnostic support. 2020 IEEE/RSJ International Conference on Intelligent Systems (ICSS)  
These systems must balance multiple, often conflicting criteria (e.g., diagnostic accuracy, treatment efficacy, risk minimization, workflow efficiency), which compounds their complexity and necessitates transparent reasoning via multi-criteria decision-making frameworks. Without clear interpretability and explainability, \"black-box\" recommendations can undermine clinician trust, impede adoption, and obscure how trade-offs are managed [3; 4]. By integrating model-agnostic explanation techniques (e.g., LIME, SHAP) with quantified metrics of separability, stability, and user experience, CDSS can reveal why specific predictions or rankings were made and how input variations affect outcomes. Such transparency not only strengthens clinical confidence and facilitates second-opinion workflows but also ensures that multi-criteria decisions remain evidence-based and auditable [5; 6]. Recent efforts have begun to integrate explainable AI (XAI) with multi-criteria decision-making (MCDM), often grounded in axiomatic frameworks that ensure fairness, consistency, and rationality. Axiomatic analyses of preference-aggregation methods (e.g., properties of TOPSIS and Borda count;) [7] and of explanation-quality metrics (identity, stability, separability) provide formal guarantees for both model ranking and interpretability. In the transportation domain, [PERSON] et al. recently proposed an \"eXplainable DEA\" approach to evaluate public-transport origin-destination efficiency, coupling Data Envelopment Analysis with SHAP-based local explanations to reveal how input factors drive efficiency scores [8]. Building on this, the same group developed an Iterative DEA (iDEA) framework for transfer-efficiency assessment in super-aging societies; iDEA alternates between DEA efficiency estimation and stakeholder-guided adjustment of input-output weights, yielding both robust performance rankings and transparent, context-sensitive insights [8]. These methodologies exemplify how axiomatic MCDM and XAI can be combined to produce decision-support tools that are not only accurate and fair but also interpretable and directly actionable by domain experts. Currently, when solving the issue of explainability, we meet several terms such as explainability and interpretability. In the XAI literature, interpretability and explainability are distinct concepts [9; 10]. We follow [PERSON] and [PERSON] [9] in defining interpretability as an intrinsic property of a model--its internal logic (e.g., decision rules or coefficients) can be directly understood without auxiliary tools.