IoT-Based Remote Patient Health Monitoring System Using Raspberry Pi and Machine Learning

This paper presents an IoT-based remote patient health monitoring system designed using Raspberry Pi 4 Model B. The system integrates MAX30100 (pulse oximeter and heart rate), AD8232 (ECG), and DHT11 (temperature and humidity) sensors to collect vital signs. A 16x2 LCD display is used for real-time visualization. Data is uploaded to ThingSpeak for cloud monitoring, while alerts are triggered using Twilio (SMS) and Mailgun (email). A Random Forest machine learning model is implemented in Python to detect anomalies in patient vitals, enabling early alerts. The project demonstrates a low-cost, scalable, and intelligent remote healthcare solution.