# Semi-Automatic Adaptation of Diagnostic Rules in the Case-Based Reasoning Process
## 2 Materials and Methods
### Case-Based Reasoning (CBR)  
Case-based reasoning has become a more well-known approach based on solving new problems using solutions of previous similar problems. Memory, learning, planning, and problem resolution are essential features of the CBR approach, providing the basis for new intelligent computing technology. This process involves adapting used solutions to meet the new requirements and using historical cases to explain the new solutions. The CBR process gained a lot of attention because the basic idea of reusing experience in solving previous problems imitates human thinking. Relying on previous experience is an intuitive and commonly used way of solving problems. In the late 20 th century, researchers examined people's ability to solve problems. They found that most people gather solutions to issues based on historical experience with similar situations. Unlike other approaches, which rely only on general knowledge of the problem area, CBR can use specific knowledge about previous problem situations [21], as also confirmed by the authors of [3]. According to [PERSON] [4], the CBR cycle consists of two main tasks. At first, the most similar case is retrieved and selected from the knowledge base. The individual cases must be compared to find the most similar ones to the new. If the knowledge base is relatively small, it is possible to perform sequential retrieval or use classification algorithms. The second task is to adapt the existing solution to match the current case and store it in the case database. If the new and retrieved cases are similar, a simple transfer of the solution is most appropriate. But if it is not the case, the adaptation is needed, which is a very complicated process. It has not yet been published a general adaptation method, and this step is entirely domain-dependent for the best of our knowledge. In this paper, we proposed an original semi-automatic adaptation model. The typical CBR life cycle consists of the RETRIEVE phase (searching for and retrieving the most similar cases), REUSE (reusing the proposed solution), REVISE (checking the proposed solution), and RETAIN (preserving) the learned case for future use). The REUSE and REVISE steps many researchers combine into a single phase called ADAPT (adaptation). The existing literature contains several models of operation of this cycle, such as the [PERSON] model, the [PERSON] model, the model by [PERSON] and [PERSON] [5], and the R4 model designed by [PERSON] and [PERSON] [5]. Model R4 is one of the most widely used and is at the highest level of generality. It can be represented by a schematic cycle containing \"four REs,\" as shown in Figure 1. The case database includes experiences that have solved problems in the past. Each step in this figure has its place and importance for the successful functioning of the CBR system. RETRIEVE phase--finding and retrieving the most similar cases. According to several authors [7; 8], this phase is one of the most important because it forms the basis for the system's overall work. The retrieving includes finding cases within those cases that are most similar to the currently examined new case. Techniques such as nearest neighbor search, inductive approaches, knowledge approaches, and validated search are the most used for retrieving in this phase. REUSE and REVISE phases--reuse and control of the proposed solution. In many practical applications, it is often difficult to distinguish between these two steps. Many researchers combine them into a single-phase called ADAPT [1]. In the early 1990s, CBR systems focused only on the retrieval step. The study of various aspects of adaptation only started after this period [10]. Most progress has also been made in the CBR cycle, retrieve, and reuse step [11]. In the reuse phase, progress has been made depending on the system's purpose, i.e., diagnosis, classification, learning, and planning (such as therapy support).