A Deep Learning H2O Framework for Emergency Prediction in Biomedical Big Data

Recently, the design and implementation of new healthcare systems have gained an interest in both industry and academia. The amalgamation between the Internet of Things, cloud, edge computing, and big data helps the proliferation of new scenarios for smart medical services and applications. Deep learning is currently paying a lot of attention for its utilization with big healthcare data. To this end, the main objective of this study is to propose a Deep Learning H2O (DLH2O) framework for improving the performance and selection of the optimal features to predict emergency cases. The proposed DLH2O framework consists of data preprocessing layer, feature selection layer and deep learning layer. The DLH2O framework aims to find the optimal subset of features and minimize the error of the classification through a proposed new variant of the Whale Optimization Algorithm (WOA) called ACP-WOA. The proposed changes have been done on the following parameters $a$ , $a2$ , $A$ , and $C$ which should affect both exploration and exploitation of WOA. The experiments conducted in order to test the validity of DLH2O Framework. In regard to the datasets, five experiments are performed for this purpose. The results demonstrate the superiority of ACP-WOA compared to the other state-of-the-art meta-heuristic algorithms in terms of time, error, and scalability. The proposed ACP-WOA is also tested on CEC2017 benchmark functions and proves its superiority over WOA in terms of accuracy.