A decision tree approach for enhancing real-time response in exigent healthcare unit using edge computing

Abstract

The aim of today's healthcare services is to provide high quality and real-time facilities and treatment options for their patients and give a patient-centric experience with full support. IoT-Based Healthcare System have improved the quality of healthcare services by enhancing its diagnosis and decision-making accuracy. On the basis of data collected from different medical Bio Sensors and Machine Learning techniques, a patient mortality and treatment can be improved with the help of current medical condition and historical Medical Health Records. In the paper a Decision Tree method has been proposed which will firstly acquire real-time medical parameter-based data from the patient through multiple BS. This data will be fed into the already trained Decision Trees in order to classify the patient into Low Risk/Normal/High Risk Category. Mobile Edge Computing technology is used in collaboration with BS in order to provide ultra-latent computation of BS-generated data and transform it into real-time decision. After severity categorization of the patient, a definite task offloading decision, whether to go for no offloading/Edge Offloading/Collaborative Edge Offloading mode will be taken. This will be done in order to facilitate severe patient with prompt treatment in case of any exigency. The proposed method outperformed Energy-Efficient Internet of Medical Things to Fog Interoperability of Task Scheduling, Optimized Latency Fog Computing and Intelligent Multimedia Data Segregation methods with a total of 88 % of improved system's performance.

Keywords

• Bio sensors
• Machine learning
• Medical health records
• Decision tree
• Mobile edge computing