关键词: 脑中风, 模糊理论, 隶属度

Abstract: In view of the shortcomings of too many stroke precursory diagnostic indicators and the fact that the diagnosis process completely relies on the doctor’s clinical experience, a new method of measurement diagnosis is proposed. Using Aprori algorithm, 16 diagnostic indicators that are highly correlated with stroke are mined, and their weights are determined based on the importance and support of these indicators. On this basis, using the extracted indicators, a risk prediction model of cerebral stroke based on fuzzy theory is constructed, and computer prediction of cerebral stroke is realized. Using the model to predict 100 outpatients, the sensitivity and specificity are 94.7% and 88.7%, respectively. Compared with the BP neural network discriminant model, the model established in this paper can more objectively evaluate the risk level of stroke in the examined population. The simulation results show that the model can objectively evaluate the stroke risk level of the examined population and provide a new method of measurement diagnosis for the early detection of stroke. At the same time, the diagnosis recommendations given by the model can provide scientific prevention and treatment measures for the examined population.

Key words: stroke, fuzzy theory, membership degree