IoT and Machine Learning Based Smart Health Monitoring System

Abstract: The rapid advancement of Internet of Things (IoT) and Machine Learning (ML) technologies has opened new avenues for developing intelligent and cost-effective healthcare monitoring systems. This paper presents the design and implementation of an IoT and Machine Learning based Smart Health Monitoring System capable of continuously monitoring multiple physiological and environmental parameters in real time. The proposed system integrates the MAX30102 sensor for heart rate and blood oxygen saturation (SpO₂) measurement, the DS18B20 sensor for body temperature monitoring, and the DHT11 sensor for ambient temperature and humidity sensing. An ESP32 microcontroller acts as the central processing unit, collecting sensor data and transmitting it to the ThingSpeak cloud platform via Wi-Fi for remote access and visualization. A 16×2 I2C LCD display provides immediate local readout of health parameters. A Random Forest machine learning algorithm, deployed through a Streamlit-based Python application, classifies the patient's health condition as Normal or Critical. Additionally, a GSM module (SIM900A) sends automated SMS alerts and voice calls to caregivers when critical conditions are detected. Experimental results demonstrate that the system achieves reliable real-time monitoring, accurate ML-based health classification, and effective emergency notification. The system is low cost, portable, and scalable, making it highly suitable for home healthcare, elderly monitoring, and remote medical applications.

Keywords: Internet of Things (IoT), Machine Learning, Health Monitoring System, ESP32, MAX30102, ThingSpeak, Random Forest, Remote Patient Monitoring, GSM Alerts.