AI-Based Prediction of Heart Attack Risk

If you're uploading your research paper to Zenodo, you can use this description: Description: This paper presents an AI-based Heart Attack Risk Prediction System that combines Internet of Things (IoT) technology with Machine Learning techniques for early detection of cardiovascular risk. The proposed system utilizes wearable sensors, including MAX30102 and MLX90614, connected to an ESP32 microcontroller to continuously monitor vital health parameters such as heart rate, blood oxygen saturation (SpO₂), and body temperature. The collected data is transmitted to a cloud platform where machine learning algorithms analyze the information and predict the likelihood of a heart attack. In high-risk situations, the system generates real-time alerts to users and healthcare providers, enabling timely medical intervention. The project aims to enhance preventive healthcare, support remote patient monitoring, and improve cardiovascular disease management through intelligent and cost-effective technology. Keywords: Artificial Intelligence, Machine Learning, Internet of Things (IoT), Heart Attack Prediction, Healthcare Monitoring, ESP32, Wearable Sensors, Cardiovascular Disease, Predictive Analytics, Remote Healthcare.