Wireless IoT and Cyber-Physical System for Health Monitoring Using Honey Badger Optimized Least-Squares Support-Vector Machine

Abstract

Health monitoring is a prominent factor in a person's daily life. Healthcare for the elderly is becoming increasingly important as the population ages and grows. The health of an Elderly patient needs frequent examination because the health deteriorates with an increasing age profile. IoT is utilized everywhere in the health industry to identify and communicate with the patients by the professional. A cyber-physical system (CPS) is used to combine physical processes with communication and computation. CPS and IoT are both wirelessly connected via information and communication technologies. The novelty of the research lies in the Honey Badger (HB) algorithm optimized Least-squares Support-Vector Machine (LS-SVM) architecture proposed in this paper for monitoring multi parameters to categorize and determine the abnormal patient details present in the dataset. Since the performance of the LS-SVM is highly dependent on the width coefficient and regularization factor, the HB algorithm is employed in this study to optimize both parameters. The HB algorithm is capable of solving the medical problem that has a complex search space and it also improves the convergence performance of the LS-SVM classifier by achieving a tradeoff between the exploration and exploitation phases. The HB optimized LS-SVM classifier predicts the patients with deteriorating health conditions and evaluates the accuracy of the results obtained. In the end, the statistical data is provided to the caretaker via a smartphone application as a monthly statistical report. The proposed model offers a Positive Predictive Value (PPV), Negative Predictive Value (NPV), and an Area Under the Curve (AUC) score of 0.9478, 0.9587, and 0.9617 respectively which is relatively higher than the conventional techniques such as Decision tree, Random Forest, and Support Vector Machine (SVM) classifier. The simulation results demonstrate that the proposed model efficiently models the sensor parameters and offers timely support to elderly patients.

Keywords: CPS; Classification; Data set; Health monitoring; IoT; Machine learning; Supervised learning.