Cloud Computing Assisted Mobile Healthcare Systems Using Distributed Data Analytic Model

Distributed cloud technologies enable mobile healthcare applications to support end users constantly. Data from electronic health records is made available through combination of user needs and heuristic mining. Because of inefficient data storage and reorganization, service and recommendation failures occur prematurely. A Distributed Data Analytics and Organization Model (DDAOM) is established in this article as a solution to the inefficiency of managing large amounts of data. Using this method, errors caused by performing several computations or storing large amounts of data in the medical field are minimized. Data organization and mining under predetermined schedules or factors provide information relevant to user services. One-to-many computations with varying input and output data allocations (for services) are executed in federated learning. The local input from several edges may be handled by allocating storage in a decentralized manner. The federated learning system uses the memory of past states to direct the allocation. Differentiating the states is necessary to allocate services and prevent mining in certain areas. With the help of realistic learning iterations, state management is maintained, guaranteeing the smooth deployment of services. Delays in storage and mining, uneven service provisioning, and service backlogs are used to evaluate the effectiveness of the suggested model.