improved cloud computing based medical information system by integrating fog computing

Abstract

The old healthcare systems are highly complex, costly, and time-consuming and it is newlinebased on a paper-based system. Now time is being changed and from the year 2016, the newlinecloud computing concept was introduced in the healthcare sector to overcome the above newlineissues. Cloud computing is used to store, process, and computes the patient data. But newlineonly cloud computing can t be able to fulfill the requirement of critical patients due to newlinethe slow response. Quality of services has also become an issue for the cloud based newlinesystem. The physiological state of the patient gets changed with time and to monitor the newlineremote patients, quick action and rapid responses are required. A tiny delay can be a newlinereason for the loss of a patient s precious life. newlineIn this thesis, to overcome the aforementioned issues in healthcare using cloud newlinecomputing is being resolved by fog computing. Now fog computing introduces in newlinehealthcare as a catalyst to improve the power of cloud computing. Fog computing is newlineused to reduce the latency for high-risk patients and enhances the quality of services for newlinethe patients. Patient s health data is classified through seven machine learning classifiers newlineand the best machine learning classifier is selected on the designed performance newlinematrices. The data is filtered into three categories such as high-risk, low-risk, and newlinenormal data. Then fog computing is processed the high-risk data. Low-risk and normal newlinehealth data of the patient is directly sent to the cloud for processing. A novel framework newlineis proposed to reduce the overall latency (transmission delay, computation delay, and newlinenetwork delay) and to improve the quality of services. The simulation results verified the newlinereduction in latency and enhancement in the quality of services. After the processing of newlinethe high-risk health data, it is sent to the cloud for further processing and to store for newlinefuture usage such as billing, records, etc. The proposed work achieved an average newlinetransmission delay of 76.834 ms, network delay of 73.4 ms, computation delay of newline273.886 ms and 81.4%