Refined Machine Learning Approach for IoT and Fog Computing Based Health Care Monitoring System

Abstract

The integration of Internet of Things (IoT) and Fog Computing technologies has paved the way for advanced healthcare monitoring systems that offer real-time, efficient, and scalable solutions for patient care. This paper proposes a refined machine learning (ML) approach to enhance the performance of IoT-based health monitoring systems, leveraging the computational power of fog nodes for data processing and analysis. The system collects health data from various IoT-enabled medical devices, including sensors for heart rate, blood pressure, glucose levels, and respiratory patterns. These data are pre-processed, filtered, and sent to fog nodes, where ML algorithms, such as decision trees, support vector machines (SVM), and deep learning models, are applied to detect anomalies, predict health conditions, and provide timely alerts. The refined ML approach involves optimizing feature selection, improving model accuracy, and reducing the latency of decision-making, all while ensuring efficient use of network resources. By distributing computational tasks to fog nodes close to the data sources, the system reduces the need for cloud-based processing, ensuring faster response times and lower bandwidth requirements. The proposed framework is evaluated in terms of its scalability, accuracy, and real-time performance in diverse healthcare scenarios. Experimental results show significant improvements in health condition prediction, anomaly detection, and energy efficiency when compared to traditional cloud-based solutions. This research highlights the potential of combining IoT, fog computing, and machine learning to build a robust, efficient, and real-time healthcare monitoring system, improving patient outcomes and facilitating continuous health management in smart environments.