IoT and Artificial Intelligence Based Heart Disease Prediction for Rural Communities

Abstract

Over 17 million deaths a year are attributed to cardiovascular diseases (CVDs), making them one of the world’s leading causes of mortality. This problem is made worse by a lack of access to high-quality healthcare facilities, particularly in rural and impoverished areas. The goal of this project is to develop an Internet of Things (IoT)-based system for early detection of cardiac disease and accessible cardiac screening. The system collects real-time data on pulse rate and blood oxygen saturation using a MAX30100 sensor module. An ESP8266 module is used to wirelessly transfer this data to a central database. Manual input of additional patient data into the system includes age, gender, and symptoms. The database integrates with a machine learning model trained to analyze the parameters and predict likelihood of different heart conditions. The system has separate doctor and patient portals. Doctors can view medical reports with patient vitals and model predictions to prescribe medications or lifestyle changes. The key innovation is using MAX30100 sensor data and ML algorithms for quick cardiac irregularity detection and disease prediction without needing in-person doctor consultations.