An adaptive secure internet of things and cloud based disease classification strategy for smart healthcare industry

Abstract

Hospital facilities were limited in rural areas and there is no awareness about disease infection and so on. Hence, the Internet of Things (IoT) technology was designed in the health care industry to treat and save illiterate people from the harmful diseases. Recently, the health care system based on IoT technology became a huge demand in the online and medical industry. However, offering the protection frame for gathered data in cloud becomes a challenging task, because the cloud contains a lot of different patient data. To overcome this issue, the current research has designed a novel Elapid Encryption in cloud frame to secure the gathered data. Moreover, the security function is executed by encrypting the collected information in the cloud storage. Also, a novel generalized fuzzy intelligence and ant lion optimization model was developed for disease prediction and severity calculation. Hence, the developed design is implemented using MATLAB and its efficiency is compared with the existing approaches such as H-DT, DNN, and DTNNN. From the comparison, proposed model has finest and highest performance like high accuracy, precision, recall and confidential rate then lower error rate and processing time. Consequently, AUC value by the developed model is 89.8%, sensitivity rate as 99% and specificity rate as 97.8%, less error rate as 0.08, accuracy rate as 99.92% and 99.9% of precision, high recall measure as 99.92%, time consumption of the proposed model is 10 s.