An Effective Prediction of Resource Using Machine Learning in Edge Environments for the Smart Healthcare Industry

Abstract

Recent modern computing and trends in digital transformation provide a smart healthcare system for predicting diseases at an early stage. In healthcare services, Internet of Things (IoT) based models play a vital role in enhancing data processing and detection. As IoT grows, processing data requires more space. Transferring the patient reports takes too much time and energy, which causes high latency and energy. To overcome this, Edge computing is the solution. The data is analysed in the edge layer to improve the utilization. This paper proposed effective prediction of resource allocation and prediction models using IoT and Edge, which are suitable for healthcare applications. The proposed system consists of three modules: data preprocessing using filtering approaches, Resource allocation using the Deep Q network, and prediction phase using an optimised DL model called DBN-LSTM with frog leap optimization. The DL model is trained using the training health dataset, and the target field is predicted. It has been tested using the sensed data from the IoT layer, and the patient health status is expected to take appropriate actions. With timely prediction using edge devices, doctors and patients conveniently take necessary actions. The primary objective of this system is to secure low latency by improving the quality of service (QoS) metrics such as makespan, ARU, LBL, TAT, and accuracy. The deep reinforcement learning approach is employed due to its considerable acceptance for resource allocation. Compared to the state-of-the-art approaches, the proposed system obtained reduced makespan by increasing the average resource utilization and load balancing, which is suitable for accurate real-time analysis of patient health status.