国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

In order to improve the privacy of medical data and effectively predict diseases, for patient medical data collected from Internet of things (IoT) devices, this paper constructs a privacy protection and disease prediction system framework for medical systems. By encrypting the combined text to establish a key, it improves the privacy of the system authentication stage and strengthens the security of the system and information transmission. The Log of Round value-based Elliptic Curve Cryptography (LR-ECC) improves the security of the data transmission stage, so that authorized medical staff can download patient data safely on the hospital side. The classification technology of deep learning neural network (EHGA-DLNN) based on image Swarm Genetic algorithm is used to achieve effective classification and prediction of disease data in the stage of disease prediction system (DPS). The experimental results show that lr-ecc method is higher than other encryption methods in encryption time and decryption time efficiency, and can achieve a security level of 98.87%. Ehga-dlnn method has a classification accuracy of 98.35% in disease prediction.