# Intelligent System Using Data to Support Decision-Making
## 4 Discussion  
Testing the system with doctors at the Clinic of Infectology and Travel Medicine at UNLP in Kosice yielded several important insights. The system clearly and comprehensively determined the chances of survival/death, with the explainable methods selecting the relevant parameters from a medical perspective. The system has practical application potential. The models were well-explained, with each patient having different attributes influencing the prediction. The system operated quickly, and the data were easily understood. The doctors agreed that model explanations using the SHAP method were more comprehensible, and they identified Random Forest as the best machine learning model. This choice was also confirmed by the Borda evaluation method. The doctors concluded that the system has potential for use at the Clinic of Infectology and Travel Medicine. They also proposed recommendations to further improve and streamline the use of the CDSS-EQCM, such as adjustments for specific parameters we can influence (e.g., the medications used) and the option to enter a specific patient currently admitted in the clinic to evaluate his/her prognosis of survival or death. Based on the extensive development process, system design, and testing by expert physicians, our CDSS-EQCM system has demonstrated strong potential for clinical application. The evaluation process, guided by the CRISP-DM methodology, incorporated both objective metrics and subjective feedback from domain experts. The key findings and practical implications are summarized below. 1. System Performance and Accuracy: During testing, all system responses matched the expected outputs, confirming reliability. To prevent overfitting, we held out 20% of the data as an unseen test set (stratified by class) and applied stratified five-fold cross-validation on the remaining 80% for hyperparameter tuning. For each configuration, we tracked the mean and standard deviation of the F\({}_{1}\)-score across folds (all \(\sigma\)F\({}_{1}\) < 0.02), retrained the best model on the full training split, and then evaluated on the test set. The gap between training and test accuracy was negligible (<0.01--for example, Random Forest achieved 0.98 on both), and precision, recall, and F\({}_{1}\)-scores aligned within 1-2%. These low-variance results demonstrate robust generalization without overtraining. Finally, integrating LIME and SHAP enabled transparent identification of key predictive features, further reinforcing confidence in model behavior on unseen data. 2. Interpretability and Usability: Physicians evaluated the interpretability of the models using metrics like accuracy, separability, stability, and subjective measures (understandability, user experience, and response time). The multi-criteria decision-making framework--implemented through the TOPSIS and Borda methods--allowed for an ordered ranking of models according to the experts' preferences. Feedback collected via a questionnaire based on a Likert scale revealed that the explanations provided by the system are clear and comprehensible. In particular, the SHAP visualizations were found to be more intuitive, which was corroborated by the Borda-based ranking. 3. Data Handling and Adaptability: The system processed multiple COVID-19 data waves, addressing challenges such as missing values and inconsistent data types through two phases of rigorous preprocessing. The flexible data-handling pipeline ensures that the system can adapt to both synthetic and real patient data, making it applicable for diverse clinical environments. Real data from the UNLP Kosice were successfully incorporated after additional modifications, proving the system's adaptability. 4.