A decision tree approach for enhancing real-time response in exigent healthcare unit using edge computing
Eram Fatima Siddiqui, Tasneem Ahmed, Sandeep Kumar Nayak
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.