# Intelligent System Using Data to Support Decision-Making
###### Abstract
2019 [16] propose the notion of causability as the degree to which an explanation to a human expert reaches a specified level of causal understanding with effectiveness, efficiency, and satisfaction in a specified context of use. In other words, causability is a person's ability to understand the explanations of ML models. Subsequently, [PERSON] et al. 2020 [5] were the first to focus on measuring the quality of individual explanations or interpretable models and propose a system causability scale (SCS) metric in combination with concepts adapted from a generally accepted usability scale. Based on many years of cooperation with physicians and several joint research projects, where we constantly generated a variety of models and visualizations, we found that understanding all the models is time-consuming for physicians. Therefore, we proposed an evaluation mechanism based on multi-criteria decision-making combining various metrics for the explainability of decision models. The TOPSIS method and the Borda method determined which created model is the best from the point of view of the preferences of the target user. Also, transparent explanations of how the algorithms arrive at their prediction through the explainable methods LIME and SHAP will be available. By providing transparent interpretations of prediction models, we can build confidence in the predictions and increase the likelihood of the algorithm being adopted in clinical practice. The results generated can help understand the factors that influence prediction. Related Work  
[PERSON] et al. [17] propose an expert-in-the-loop methodology for ADHD prediction that iteratively refines Decision Trees to balance accuracy and interpretability. Starting with an automatically generated tree, medical experts review and adjust its structure; the final model achieved 73.2% test accuracy, demonstrating that incorporating domain knowledge can yield more meaningful decision rules without severely sacrificing performance. [PERSON] et al. [3] introduce LIME, a model-agnostic explanation technique validated on text (SVM, RF, LR, DT) and image (deep networks) classifiers. Through simulated user studies, they show that LIME explanations increase both expert and non-expert trust and understanding across diverse tasks, underlining its utility for verifying model behavior and fostering confidence in predictions. [PERSON] et al. [18] analyze four ML algorithms (DT, RF, GBT, NN) on early COVID-19 patient data and apply PDP, ICE, ALE, LIME, and SHAP to identify key biomarkers of disease severity. Local explanations from SHAP and LIME were rated by physicians, and all models matched expert diagnoses. Similarly, study [19] evaluates LIME outputs on a 13,713-record sepsis dataset using AdaBoost, RF, and SVM; RF (ntree = 300) achieved top performance, and clinicians preferred graphical over textual explanations (4.3/5). [PERSON] et al. [20] apply LR, RF, and SVM to 5647 Brazilian COVID-19 cases, prioritizing F\({}_{2}\) to capture true positives; SHAP interpretations for RF and SVM enhanced both local and global transparency. [PERSON] et al. [21] develop xMLEPS--an explainable LightGBM ensemble--for early ICU sepsis prediction, employing ten-fold validation and SHAP plots to elucidate physiological relationships and ensure clinical generalizability. [PERSON] et al. [22] integrate clinical, laboratory, and chest X-ray radiomic features (1589 patients) using RF and SVM within a multi-level explainability framework: global feature importance and local patient-specific rationales. [PERSON] et al. [23] combine MLP and DT models with LIME to predict COVID-19 severity, achieving up to 80% accuracy and providing individualized condition insights via a mobile application for medical staff. Study [24] benchmarks six interpretability methods (LIME, SHAP, Anchors, ILIME, LORE, MAPLE) on RF models across identity, stability, separability, runtime, bias detection, and confidence metrics, finding no universal best. [PERSON] et al.