Abstract
Cardiovascular disease has the characteristics of high morbidity, high disability rate and high mortality, that seriously endangers people's health and life. It is of great significance for the prevention and early diagnosis of cardiovascular disease. With the assistant diagnosis method of cardiovascular disease based on case-based reasoning (CBR), the cardiovascular status represented by the current pulse waveform can be diagnosed by the historical pulse waveform. Firstly, the case base is constructed by the existing cases of cardiovascular disease. When there are new cases to be diagnosed, the similar cases will be retrieved by case retrieval, and the recommended diagnostic results of the new case will be obtained by the case reuse. In order to improve the accuracy of case diagnosis, three weight allocation and optimization methods, namely, average weight, genetic algorithm optimal allocation weight and introspective learning dynamic adjustment weight, were compared. The experimental results show that weight optimization method with introspective learning can significantly improve the accuracy and the ROC (Receiver Operating Characteristic) performance of diagnosis.
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