Research on Auxiliary Diagnosis Technology of Chronic Disease Based on Case-Based Reasoning

In recent years, with the continuous acceleration of social progress, the number of patients with chronic diseases worldwide has increased year by year, and the pressure on medical institutions and volunteer workers has also increased. Especially in the current situation of rampant CoviD-19, if the number of admissions and treatments for chronic patients can be reduced, it will help reduce the pressure on hospitals and medical staff during the special period at this stage, and will also reduce the spread and infection rate of CoviD-19. The research team established big health data through questionnaire surveys, and established a case database based on these data, and proposed an assisted diagnosis technology for chronic diseases based on case-based reasoning. With the continuous expansion of the application field of Case-Based Reasoning (CBR) technology, the problem of programmers' difficulty in obtaining and expressing professional knowledge has become increasingly prominent. Therefore, this article first gives the standardized expression of health data, and combines the method based on case-based reasoning with the scientific measurement of keyword weight (Term Frequency-Inverse Document Frequency, TF-IDF) to give the weight in the case retrieval process It also designs the calculation of the dispersion degree between classes for the distribution of characteristic words, which provides an effective method for the establishment of an auxiliary diagnosis model for chronic diseases.