Heart Disease Prediction using Fog Computing based Wireless Body Sensor Networks (WSNs)

Abstract

Wireless Body Sensor Network (BSNs) are devices that can be worn by human beings. They have sensors with transmission, computation, storage and varying sensing qualities. When there are multiple devices to obtain data from, it is necessary to merge these data to avoid errors from being transmitted, resulting in a high quality fused data. In this proposed work, we have designed a data fusion approach with the help of data obtained from the BSNs, using Fog computing. Everyday activities are gathered in the form of data using an array of sensors which are then merged together to form high quality data. The data so obtained is then given as the input to ensemble classifier to predict heart-related diseases at an early stage. Using a fog computing environment, the data collector is established and the computation process is done with a decentralised system. A final output is produced on combining the result of the nodes using the fog computing database. A novel kernel random data collector is used for classification purpose to result in an improved quality. Experimental analysis indicates an accuracy of 96% where the depth is about 10 with an estimator count of 45 along with 7 features parameters considered.

Modified Self-Adaptive Bayesian Algorithm for Smart Heart Disease Prediction in IoT System

“…The data collector is set up, and the calculation is carried out using a decentralized system using a fog computing environment. After integrating the nodes' results using the fog computing database, a final output is generated [17].…”

“…In this section, we evaluate the efficacy of our suggested approach using the performance indicator indicated before. Our proposed technique is performed and also matched with other standard techniques (MANAGE-HF [50], GA [51], ML [52], BSNs [17], MSABA). Heart failure patients experience a range of physical and psychological symptoms, including dyspnea, fatigue, edema, trouble sleeping, sadness, and chest discomfort.…”

“…The suggested algorithm's efficacy is evaluated using these metrics. The outcomes were compared to those obtained using conventional methods such as Multiple Cardiac Sensors for the management of heart failure (MANAGE-HF) [50], genetic algorithms (GA) [51], machine learning approaches (ML) [52], and body sensor networks (BSNs) [17]. As per data collection, the datasets are gathered out of public health datasets.…”

Performance evaluation of compact FSS‐integrated flexible monopole antenna for body area communication applications

“…The rejection frequency depends on the capacitance and inductance produced by the conductor portion of FSS. The proposed FSS is inspired by square loop FSS, 39–41 and the resonance frequency is given as Equation . The spacing decides the capacitance of FSS between Unit cells (2L g ) and the periodicity of unit cells.…”