HeartGuard: An IoT-Enabled Cardiovascular Health Monitoring and Risk Prediction System

HeartGuard is a prototype system that uses Internet of Things (IoT) technology to monitor cardiovascular health and predict risks. It combines physiological sensing, ECG data capture, wireless communication, database storage, and patient medical profiles within one integrated platform. The hardware centers on an ESP32 microcontroller and includes sensors for heart rate, oxygen saturation (SpO₂), temperature, and ECG. The system displays data locally on an OLED screen and also sends it to a web application, allowing remote data viewing. The software integrates real-time sensor readings with patient details such as age, sex, blood pressure, cholesterol, chest-pain type, exercise-induced angina, ST depression, and resting ECG results. For risk prediction, HeartGuard uses a Random Forest machine learning model, while ECG arrhythmia classification relies on a convolutional neural network (CNN). Currently, these models are developed using publicly available data from Kaggle. In the future, HeartGuard aims to train models directly on data collected by the deployed sensors. The prototype presents cardiovascular risk as a percentage calculated at the application level, but future versions plan to use calibrated probabilities directly from the Random Forest output. The system includes interactive interfaces for risk visualization, ECG analysis, patient records management, and generates clinical-style PDF reports.