A Hybrid Real-time remote monitoring framework with NB-WOA algorithm for patients with chronic diseases

The embracing of the Internet of Things (IoT) and Cloud Computing technologies gives excellent opportunities to develop smart healthcare services that have great prediction capabilities. This paper proposes a Hybrid Real-time Remote Monitoring (HRRM) framework, which remote-monitors patients continuously. This smart framework predicts the real health statuses of the patients in real time by using context awareness. The proposed HRRM framework innovates a Patient's Local Module (PLM) that do a convergence between IoT sensors and clouds. The HRMM transfers some of the computations to the edge of the network in (PLM) such as converting the low-level data to a higher level of abstraction to speedup the computations in the cloud portion of the HRMM. The convergence of loT enables the HRMM to use the enormous cloud power in storing, processing, analyzing big data, building classification models for the category of patients' health status. The local portion of the HRMM uses classification models that have been trained in the cloud to predict the health status of the patient locally in the event of internet interruption or cloud disconnection to save his life in the disconnection periods. Furthermore, this paper proposes a cloud classification technique that is capable of dealing with big imbalanced dataset by minimizing errors especially in the minority class that represents the critical situations. Finally, a hybrid algorithm of Naive Bayes (NB) and Whale Optimization Algorithm (WOA) has been proposed to select the minimal set of features that achieve the highest accuracy. The (NB-WOA) works as a safe-failure module that decides when to stop the monitoring using HRMM in the case of the failure of influential sensors. Experiments have proved that the HRMM is capable of predicting the health status of the patients suffering from blood pressure disorders accurately. Also, it proved that NB-WOA accelerates the classification process and saves storage space. (C) 2018 Elsevier B.V. All rights reserved.