An Efficient IoT-Driven Health Care Monitoring System using Advanced Metaheuristic Optimisation Algorithms with Spiking Neural Network for Smart Diagnosis

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 11

Year of Publication : 2025

Authors : Fatima Alqahtani, Betty Elezebeth Samuel, Rasitha Banu GulMohamed, Vidya Sivalingam, Anjali Gupta, Nithinsha Shajahan

10.14445/23488549/IJECE-V12I11P105

How to Cite?

Fatima Alqahtani, Betty Elezebeth Samuel, Rasitha Banu GulMohamed, Vidya Sivalingam, Anjali Gupta, Nithinsha Shajahan, "An Efficient IoT-Driven Health Care Monitoring System using Advanced Metaheuristic Optimisation Algorithms with Spiking Neural Network for Smart Diagnosis," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 11, pp. 43-56, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I11P105

Abstract:

Since the population is rising worldwide, a vast need arises to deliver appropriate medical care services. The sensor is an effective technology primarily employed to enable the Internet of Things (IoT)- based healthcare monitoring method. The IoT is transporting a novel revolution in research and academia. It has powerful roots, which are producing amazing variations in numerous areas, especially healthcare. IoT healthcare methods enable patients to receive personalized care by remotely monitoring their conditions. IoT applications are primarily beneficial for delivering healthcare, as they allow secure and real-time remote patient monitoring. In recent times, the traditional linear method has been replaced by innovative techniques of Artificial Intelligence (AI) and Machine Learning (ML). Whereas, Deep Learning (DL) is a sub-field of ML, which is much more trustworthy and stronger to certainly manage and study from a vast quantity of intricate healthcare data, and provides actionable visions and solutions to complex issues. This study proposes an Efficient Health Care Monitoring System using Advanced Metaheuristic Optimisation Algorithms and Spiking Neural Network Method for Smart Diagnosis (EHCMS-MOASNN) model for Smart Diagnosis in IoT. The primary objective of the EHCMS-MOASNN technique is to develop a smart healthcare monitoring system for the medical sector utilizing advanced models. Initially, the data pre-processing applies the min-max scaling method to convert input data into an appropriate format. Furthermore, the feature selection process is implemented using the Binary Grouper and Moray Eel (BGME) optimization approach to detect and select the most relevant and significant features in the input data. For the classification process, the EHCMS-MOASNN technique implements the Spiking Neural Network (SNN) approach. Additionally, the Mountain Gazelle Optimiser (MGO)-based hyperparameter tuning is performed. The comparison analysis of the EHCMS-MOASNN method demonstrated a superior accuracy value of 99.12% over recent techniques under the Healthcare IoT dataset.

Keywords:

Health Care Monitoring, IoT, Metaheuristic Optimisation Algorithms, Spiking Neural Network, Smart Diagnosis.

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