# Intelligent System Using Data to Support Decision-Making
###### Abstract
By contrast, explainability refers to post hoc analyses that attach human-readable justifications to a model's output, even when the model itself is a black box (e.g., deep nets or Random Forests). Interpretability is the ability of a model to be easy to understand from its very essence--that is, without additional tools or explanations. Interpretability means that the model is understandable just by looking at it. Explainability refers to the model's ability to explain why it made the decisions it made. In other words, explainability answers the question: \"Why did this model make a particular prediction?\". Explainability focuses on the creation of additional tools (e.g., LIME, SHAP, Partial Dependence Plots) that help users understand the logic of the decision, even if the model itself is complex and non-transparent (\"black box\") [6]. In this work, we train two inherently interpretable models (Decision Tree, Logistic Regression) and three black-box models (Random Forest, k-NN, SVM). Because clinicians found the raw tree diagrams and coefficient tables difficult to parse, we also applied LIME and SHAP to the Decision Tree and Logistic Regression, ensuring that every model--interpretable or black box--produces intuitive, visual explanations. Throughout, we reserve interpretability for models whose structure and parameters admit direct human understanding, and explainability for algorithmic routines that generate post hoc insights into any classifier's behavior. Whenever clinicians require more digestible visuals, we employ explainability methods (LIME, SHAP) even on interpretable models. In medical applications, achieving high prediction accuracy is often as crucial as understanding the prediction [11]. [PERSON] et al. [12] describe the primary goal of explainabilityas the ability to explain the ins and outs of the system in an understandable way to end users. We can understand an explainable model, and it is related to accuracy, efficiency, and explainability. In addition, interpretability is often associated with a new wave of ML research. In this case, it provides confidence in the ML system's development process for end users of such systems [13]. The explanation of ML models is often driven by different motivations, such as explainability requirements, the impact of high-risk decision-making, societal concerns, and ML requirements, regulations, and others. There are already several different definitions for the term explainability. However, we will follow the opinion of [PERSON] [6] and [PERSON] [10], i.e., that explainability (in the context of AI and ML) refers to the ability of a model or algorithm to provide a clear and understandable explanation of how it arrived at its decisions or predictions. The goal is to remove the need for users (such as analysts, targets, or end users) to understand why the model performed a particular need or provided a particular output. If we talk about interpretable explanations of individual models, several methods exist to interpret the result in a comprehensible and trustworthy way. They point out why the model decided in this way and what led it to do so. Without robust explainability techniques, it would not be easy to understand these decisions. In our work, we considered the following methods: Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive ExPlanations (SHAPs). Understanding the reasons for predictions, causes and recommendations is essential for several reasons. One of the most important reasons is the trustworthiness of the result [14]. Moreover, the next area that needs to be considered is the direct measurement of the user's perception of the utility of the explanations, including the causal considerations, using a good usability approach. Then, it follows by measuring the user experience, i.e., the usability of the software products [15]. Efficiency, effectiveness, and user satisfaction need to be considered when measuring interface usability. [PERSON] et al.