Integrating IoT and Machine Learning for Real-Time Patient Health Monitoring with Sensor Networks

An innovative approach for continuous health monitoring in medical applications is presented in this research. The proposed system is composed of Raspberry Pi, cloud storage, machine learning, and IoT sensor. The IoT sensor monitors patients' vitals in real time and quickly identifies any anomalies. The patient wearing the sensors transmit the real-time data with Raspberry Pi processors. The Raspberry Pi collects the real time data from sensors such temperature, blood pressure, heart rate, and pulse oximeter. Then the IoT transmits the data collected to a cloud server. K-Nearest Neighbors (KNN) is a data processing and analysis method used in the cloud server. The KNN algorithm categorizes and analyzes the data collected, discovers the trend and anomalies present in the patient's vital signs. The proposed system has a simple user interface that can be accessed via a web or mobile application, allowing doctors and nurses to remotely look at the patient's data and generate real-time alerts in case of severe health situations. While cloud technology ensures scalability, data storage, and advanced analytics, the integration of Raspberry Pi devices makes it possible to process data locally and reduce latency.

I. Introduction

The IoT is advancing quickly in medical and health fields. IoT Creative technology and resources are advancing health wearable devices. Health wearables may track in/out patients' health. The E-Healthcare Monitoring solution (EHMS) is an Internet-of-Things (IoT) application framework that employs ML to create an advanced automation solution. This system connects, monitors, and makes diagnoses [1]. It creates an intelligent system for health monitoring using IoT and machine learning. According to the system, IoT devices gather health data from people and use machine learning for analysis and prediction. IoT devices will monitor and analyze vital signs and other bio-signals in real time. Machine learning allows the system to learn from data, find trends, and anticipate health issues accurately. The installation and advantages of the system for enhancing healthcare management and monitoring are discussed in [2]. An intense healthcare monitoring system using IoT and machine learning propose collecting patient healthcare data using IoT devices and analyzing it with machine learning. IoT sensors and machine learning algorithms monitor patients' health issues in real-time. The suggested paradigm and its potential to improve healthcare monitoring are discussed [3].