• Chetana Sanjay Chaudhray
• Dr.Brijendra Gupta

The initiative centers on the creation of a health monitoring system utilizing Internet of Things (IoT) technology, designed to forecast the likelihood of a heart attack by assessing critical physiological indicators, including Blood Oxygen Saturation (SpO2), Heart Rate (BPM), and Body Temperature. These metrics are collected through a NodeMCU ESP32 microcontroller, which is equipped with a MAX30102 Pulse Oximeter and Heart Rate Sensor, as well as an MLX90614 Infrared Temperature Sensor. The gathered data is subsequently transmitted to a Firebase Realtime Database for processing, utilizing the Arduino IDE. On the client side, an Android application accesses the data from Firebase and employs a Decision Tree Machine Learning Algorithm to evaluate the heart attack risk. The system classifies the risk into three categories based on the input parameters: No Risk, Medium Risk, or High Risk. The objective of this project is to facilitate real-time monitoring and provide early alerts to individuals at risk of cardiovascular complications, thereby allowing for prompt medical response.

Health monitoring system, heart attack prediction, heart rate, blood oxygen saturation, body temperature, firebase real-time database.

"Health Monitoring System for Heart Attack Risk Prediction using IoT and Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.a421-a424, November-2024, Available :http://www.jetir.org/papers/JETIR2411045.pdf

"Health Monitoring System for Heart Attack Risk Prediction using IoT and Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. ppa421-a424, November-2024, Available at : http://www.jetir.org/papers/JETIR2411045.pdf