Abstract

Internet of Things (IoT) and cloud computing technologies offers several applications in healthcare sector. On a distributed healthcare management, a number of IoT devices are utilized for monitoring the health conditions of the patients and transmit the data to the cloud server for further processing. This paper introduces a new cloud and IoT based distributed healthcare system using improved particle swarm optimization (IPSO) with support vector machine (SVM), named IPSO-SVM model. The proposed method initially involves the data acquisition process, where the data will be generated using IoT devices and benchmark medical data repositories. The IPSO-SVM model will be trained using repository data in the cloud server and is thereby employed to test the patient's data transmitted from the IoT devices. The proposed IPSO-SVM model performs disease diagnosis process in the cloud, which identifies the existence of disease effectively. At last, the generated test reports will be sent back to the patient's, healthcare centres, and physicians. A series of experiments takes place on diabetes disease to verify the effective performance of the proposed IPSO-SVM model. The simulation outcome indicated that the IPSO-SVM model has offered superior results with the maximum average sensitivity of 96.28%, specificity of 93.72% and accuracy of 94.44%.

Description