# Semi-Automatic Adaptation of Diagnostic Rules in the Case-Based Reasoning Process
## 1 Introduction  
Machine learning promises significant advancement in the healthcare domain up to personalized medicine [1]. Currently, the most often used are predictive models, trained on various sorts of medical data. Predictions include the use of advanced analytical technology, modeling, machine learning, and data mining algorithms, running simulations, and pattern searching. Some machine learning models provide excellent predictive accuracy but do not offer understandable insights about their predictions. This is a severe problem in the medical domain. Explaining data patterns is useful in every way to understand and trust the system and its predictions. Data scientists interpret large amounts of data, use analytical algorithms, and convey knowledge through visual and textual descriptions to help people understand details. Today, healthcare providers generate and collect large amounts of data and gradually deploy analytical solutions of various types. This situation requires effective data processing and analytics with results in a simple, understandable form for target users represented, e.g., by general practitioners, doctors, specialists, or researchers. It is not easy for a doctor to consider a large amount of data when diagnosing or determining the right treatment. Due to its complexity, this process is prone to errors and widespread misdiagnosis. There are many cases in which the symptoms and diseases overlap and may be characteristic of several diseases. According to the World Health Organization (WHO), up to 5% of outpatients were misdiagnosed in 2015, which is a very worrying result. Thus, the correct diagnosis of a disease or condition relies on the availability of all the necessaryinformation. The volume, complexity, and dynamics of this situation challenge doctors and other health professionals. The number of symptoms is non-specific and variable depending on the case; many diagnostic tests are expensive, irregular, and sometimes do not give the correct answer. Also, physicians usually rely on their cognitive abilities and experience in diagnosing similar or identical symptoms. Given the growing diversity of treatments and medicines, the speed of data collection, the availability of diagnostic tests, and the differences of opinion between doctors, the use of a diagnostic decision support system can help. Traditional decision support models use rules extracted from the historical data to evaluate new cases. However, what is to be done if the new case does not meet any of the existing rules? The typical approach represents the re-generation of all rules based on the extended data sample and requires the time and effort of a data or knowledge engineer who may not be available at the time. It is not ideal in clinical practice, where new patients or the same patients with different medical conditions come every day. This problem is partially solved by case-based reasoning method, which uses the knowledge and solutions used in past cases based on their similarity to the new case. The case-based reasoning (CBR) method predicts future situations based on similar historical conditions. This approach itself is not entirely new, but after reading a number of studies dealing with case-based reasoning [2; 3; 4; 5; 6; 7; 8] and its applications in medical data [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21], we found that this area still offers several research problems that have not yet received sufficient attention, or a suitable solution has not previously been proposed and tested [9]. One of them is a semi-automatic adaptation of decision models using expert knowledge. In medicine, the adaptation phase of the CBR is a significant challenge, which is avoided by several systems using the CBR method. This fact motivated us to propose an approach to solving this problem without a need to regenerate the decision models for each new case.