## 4 Research and Project Aimed at Developing Modules of a Classification and Expert System
### Final Classification After Treatment Module
#### 4.3.4 Implementation of Results in the System Module  
In the final outcome assessment module, the implementation incorporates visualization tools to enhance interpretability and facilitate medical staff's decision-making process. Figures 11 and 12 represent two examples of key components included in the module interface.  
Figure 11 displays the probability distribution for a specific instance across all potential classification outcomes. As shown in Figure 11, the system computes and presents the likelihood of each class, allowing medical professionals to assess the confidence associated with the predicted outcome. In this example, the random forest classifier assigned a dominant probability of 0.58 to Class 3 while attributing 0.34 to Class 5, reflecting a nuanced differentiation between similar categories. The remaining classes received probabilities close to zero, indicating minimal ambiguity in the classification for these outcomes.  
Figure 12 provides insights into the importance of individual features in the classification process, as determined by the random forest algorithm. In Figure 12, features are ranked based on their contribution to the model's decision-making. The top features (_feature12, feature1, and feature2_) exhibit the highest importance scores (0.15, 0.14, and 0.14), while lower-ranked features (_feature3 and feature4_) have minimal impact. Such insights are invaluable for understanding which clinical parameters play a critical role in the predictive model, aiding in interpretability and potential model refinement.  
Both figures, integrated into the application interface (Figure 13), provide a comprehensive overview to support medical professionals in evaluating system predictions. By presenting both the probabilistic outputs and the underlying feature contributions, the module ensures transparency, allowing domain experts to validate and trust the system's recommendations in real-world scenarios. This approach enhances the system's utility by bridging the gap between automated predictions and clinical expertise.  
Figure 11: Example overview of probability indications for a given prediction class.  
Figure 13 illustrates the user interface of the final outcome assessment module within the application. This graphical window integrates multiple functionalities to support the medical personnel in reviewing and validating the system's predictions. On the right side of the interface, two key visualizations are displayed. The feature importance analysis  
Figure 12: Example overview of the importance indications for individual classification features.  
Figure 13: Application interface for the final outcome assessment module (third module from Figure 1).  
bar chart (top-right) highlights the contribution of individual features to the classification process. The class probability distribution chart (bottom-right) provides the likelihood of the prediction for each class, enabling the clinician to assess the confidence of the assigned classification.  
On the left side of the interface, direct clinical questions are presented. Below these questions, two options (Yes or No) are provided, allowing the user to confirm or deny, based on the patient's clinical condition. The system then presents a definition of a given final assessment class along with its justification. The module's layout is designed for clarity and usability, ensuring that clinicians can interact with the system effortlessly while interpreting predictions in real time.