Prediction model of sleep membership in drugstores based on immune danger theory

The artificial immune danger theory is introduced into the analysis of user behavior characteristics to effectively identify the dormant member users of pharmacies. Take pharmacy members as an example, analyze the behavior characteristics of pharmacy members, select characteristic attributes such as purchasing behavior, medical history, gender and age, payment method, activity level, membership card points, etc.Use the attribute analysis result as the characteristic signal to distinguish between quasi-sleeping member users and normal users, and based on Dendritic Cells Algorithm(DCA) to realize the identification of quasi-sleeping members of pharmacies.The effectiveness of the algorithm is verified and compared with the real data of pharmacy member users. The results show that the method can effectively detect users who may become sleeping members among pharmacy members, and has a high detection accuracy rate.