Mobile cloud computing based stroke healthcare system

Information technology has recently seen a huge progress in innovative healthcare technologies that rendered healthcare data bigger. Connectivity on 7/24 basis between human to device and device to device have a crucial role in individuals' lives. Therefore, Mobile Cloud System (MCC) has become an indispensable tool. Parallel with the rapid developments in the Internet of Things, convergence has become an important issue. Our proposed method, accordingly, can be converged with mobile-cloud environments with cloud computing in handling healthcare information. This study uses Virtual Dedicated Server (VDS) as 4 VCPU and 8 GB RAM and proposes a model based on the Android based mobile phones for stroke patients with cardioembolic (689) and cryptogenic (528) subtypes. The system set up through this study has two basic application elements which are mobile application and server application. Artificial Neural Network (ANN) module is beneficial for classifying the two stroke subtypes while server application is used for saving the data from the patients. Accordingly, our model guarantees availability, security, and scalability as a system for stroke patients applying Stroke dataset for ANN algorithm, Multilayer Perceptron Algorithm (MLP), which has been done for the first time in literature with big data in this scope. The main contributions are: (1) The outcomes will display an individual unique social insurance framework. (2) The outcomes will be utilized for the distinguishing proof of stroke-related data to be gathered by cell phones that are Android based. (3) Stroke patients will find out about their condition of wellbeing through an ANN application programming interface, which will provide a sort of organization for the patients. Overall, an efficient and user-friendly stroke determination human services framework has been presented through this Healthcare System for patients.