No abstract

The issues of detailing recognition algorithms in order to increase the validity of their solutions in diagnosing patients are considered using the example of processing nephrology data. The training of algorithms with a teacher is implied. Procedures for detailing complexes of clinical signs and criteria for comparing such complexes in decision-making are proposed. This means dividing these objects into elements, extracting additional information for them from a priori and current data, and taking them into account in algorithms. Research in the work was focused on the development of software tools for detecting and evaluating additional reserves and opportunities for improving the quality of decisions of recognition procedures by extracting additional useful information from a priori and current data and using them in the process of detailing decision-making procedures. On a specific algorithm, various approaches to such detailing and to the study of its effectiveness were analyzed. Such detailing can be built on the basis of using the experience of clinical practice of observation of patients and their diagnosis in the form of training samples of symptom complexes and (or) observed signals in clinical cases with reliably confirmed diagnoses in the relevant databases. Detailing these algorithmic procedures can lead to the emergence of a multi variance of possible solutions for differently detailed algorithms and require the use of additional procedures for generating a generalizing conclusion based on the results of their mutual consultation. The order and results of detailing are demonstrated in the MatLab environment on two modifications of the proposed algorithm. The introduction reveals the relevance and content of the research. Section 1 reveals the composition of a priori patient data in demo examples and the information that is extracted from them at the training stage. Section 2 proposes two modifications of the algorithm to detalize it. Section 3 proposes software procedures for the statistical evaluation of the performance of the detalization of the algorithms under study. Section 4 describes the refinement of algorithms by introducing weights into the decision criterion, taking into account the spread of values of clinical signs. Section 5 demonstrates the detalization of the algorithms taking into account the information content of the features. The conclusions summarize the results of the work. In general, they are positive.