Intelligent Generation Technology of Sub-health Diagnosis Case Based on Case Reasoning

Diagnosis and treatment of sub-health have the characteristics of multi-factors, uncertainty of pathological criteria and complexity of management methods. Therefore, the traditional rule-based expert reasoning method has limitations in the implementation of sub-health diagnosis scheme. Considering the characteristics of sub-health, this paper uses unstructured data elements to represent sub-health diagnosis case, and establishes an intelligent generation system CBR- SDC for sub-health diagnosis cases. The improved hybrid similarity retrieval method based on fuzzy mathematics and Euler distance, and the weight adjustment strategy and algorithm of PULL&PUSH are applied to case reasoning to solve the retrieval and correction of sub-health cases. The effectiveness of the method and the efficiency of case retrieval and revision are verified by comparative experiments. It provides an efficient new method for assistant diagnosis of sub-health pathology in medical industry.