# Semi-Automatic Adaptation of Diagnostic Rules in the Case-Based Reasoning Process
## 3 Results and Discussion  
As mentioned before, we proposed a semi-automatic adaptation method of the existing case base according to a new case and expert's decision based on automatically proposed decision rule modifications. Our motivation was to prevent continuously re-generating decision models using machine learning algorithms. Finally, in this study we integrated the innovative CBR approach into our DSS and experimentally tested on available medical samples about cardiovascular disease patients. This section will briefly introduce our approach (Figure 2), published in one of our previous papers [22]. The cycle starts with information provided about a new patient (case). The user will add information about this patient by means of the user interface in the predefined format containing all necessary characteristics (input variables). In the RETRIEVE step, the new case is compared with existing historical cases stored in the case base using inference mechanism (IM). The cases are stored as IF-THEN rules extracted from the decision model generated by the decision trees algorithm described in previous section. The comparison is determined by calculating the distances between the cases using original similarity metric based on decision (sub)rules transcripts. The comparison can result in one of the following three alternatives:  
1. The IM finds a perfectly fitting decision path for the new case. The value of the new case's target attribute will be the same as corresponding decision rule suggests. The system will return the diagnosis to the user. 2. The IM does not find any at least partially relevant decision rule, i.e., all existing cases stored in the case base differ significantly from the new one. This situation requires the intervention of an expert who classifies the new case (determines the value of the target attribute). Then, the system will generate the decision rules again from the extended data set. 3. The IM returns a list of partially similar decision rules with different target attribute values. The CBR cycle will proceed with the steps REUSE, REVISE, and RETAIN. Before the cycle continues to these steps, it is necessary to examine and determine the differences between the new and similar cases. Two situations can occur:  
Figure 2: CBR cycle with semi-automatic adaptation of the stored cases. * The cases differ in only one condition (parameter) on the left side of the rule. The expert may consider modifying this rule (ADAPT phase), check the proposed classification of the new case according to this rule (REVISE phase), and then the new case will be classified according to the value of the target attribute of this rule (REUSE phase). The new case can be saved/not saved (RETAIN phase) in the case base according to the user's decision. If the system finds several cases that differ from the new case in only one condition, the expert decides on the rule containing the difference in the less severe attributes. It is also possible to assign weights to the difference according to the expert's decision. * The cases differ in several conditions (parameters) on the left side of the rule. Initially, a list of different conditions (parameters) is identified. The difference between a similar case and a new case is calculated using the similarity and score metrics. In this case, an expert can also assign the scales according to their importance to specific differences. The expert determines the value of the new case's target attribute, while our system automatically proposes the rules with the highest similarity and the lowest score to the new case (ADAPT, REUSE, REVISE phase), which can help the expert to decide. As needed, it can adapt the rules (adjust boundaries or change the target value), which can also be saved and will be immediately available for the next iteration of adding a new patient. The new case can be saved/not saved in the case database (RETAIN phase).