# Intelligent System Using Data to Support Decision-Making
## 3 Results
### Proposed Approach  
In the second version, this proposal was modified based on communication with participating doctors to achieve better comprehensibility and reduction of diagnostic time. For experimental verification we decided to design a decision support system that would provide the user, the physician, with explanations of predictions, which could improve the understanding of decision models in this area and increase the comprehensibility and trustworthiness of these models with the aim of their use in practice. After displaying individual explanations, physicians should evaluate individual explanations using metrics for evaluating the quality of explanations, with some metrics being calculated automatically and some being evaluated by physicians. Using two evaluation methods and multi-criteria decision-making, the system should finally display the order of models along with explanations in descending order and recommend to the physician which model explanation is the most interpretable, so that during the next use it would save time for physicians and only the model that is most comprehensible would be displayed to them. The prototype of the Clinical Decision Support System for evaluating the quality and comprehensibility of model's (CDSS-EQCM) was designed and implemented, see Figure 1.  
1. The user shall show the best model selected and rated by a doctor with many years of clinical experience or generate explainable representations of models in the graphical form in the implemented clinical DSS.
2. Subsequently, whether similar models are already stored in the database is checked (DB of saved TOP model).
3. If the best model is not stored yet, a domain expert (in our case, a doctor) enters the process.
4. The doctor will determine the values of the metrics.
5. In the MCDM process, the doctor then determines the metrics preferences of which metric is more important to the other. 1. The doctor chooses preferences between each metric. The values use a scale from 1 to 9, where 1 represents equal importance and 9 represents extreme importance.
6. The calculation is performed, the Saaty matrix is filled in, and the weights for each metric are calculated using the normalized geometric diameter of this matrix. The higher the weight, the more critical the metric.
7. The values of metrics and metric weights shall be used for calculations performed in the TOPSIS and Borda methods.
8. The doctor chooses to evaluate explainable models using the TOPSIS method. Steps in the TOPSIS method: 1. Compilation of the criterion matrix with alternatives (models) as rows and criteria (metrics) as columns. We assign a score (the resulting metric calculation) to each alternative for each criterion. 2. Adjust all metrics to the same extreme type. 3. Normalization of the adjusted criterion matrix. 4. Creating a weighted criterion matrix by multiplying the normalized decision matrix by the weight of each criterion.  
Figure 1: CDSS-EQCM.  
* From the weighted criterion matrix, we determine the ideal and basic models.
* Finally, we calculate the distance of the model from the ideal model and the basic model, and, using these values, we calculate the relative indicator of the distance of the model from the basic model
* The doctor chooses to evaluate explainable models using the Borda method which is calculated automatically by metaethical formula.
* The system arranges the individual models in descending order.
* The results database will store individual models, model order, values, weights, and preference metrics.
* The doctor will obtain a list of recommended models according to the Borda and TOPSIS methods.
* The doctor will decide whether to save the most understandable model according to the Borda or TOPSIS method or choose his own model that he found most understandable.