Iot Based Heart Monitoring System And Disease Prediction Using ECG And Machine Learning Algorithm

Abstract

The IoT Based Heart Monitoring System and Disease Prediction Using ECG and Machine Learning Logistic
Regression Algorithm is an innovative solution designed to continuously monitor heart health using embedded
technology and IoT. The system employs an ECG sensor to record the heart's electrical activity, which is
processed by an Atmega microcontroller. By analyzing the ECG signal patterns, the system can detect whether
the heart activity is normal or exhibits abnormalities indicative of potential heart conditions and is embedded
with GPS to identify the system’s location. The results are displayed on an LCD screen for immediate access and
transmitted via a WiFi module to a remote server, enabling real-time monitoring by healthcare providers or
caregivers using a smartphone application. This integration of IoT technology facilitates early diagnosis and
intervention, particularly for patients in remote or rural areas who might not have immediate access to medical
facilities. This system is cost-effective, portable, and user-friendly, making it an ideal choice for personal health
tracking, remote patient monitoring, and early detection of heart conditions. The project leverages a logistic
regression algorithm to predict heart disease based on ECG data, aiming to bridge the gap between advanced
healthcare technology and accessibility, ensuring timely medical responses to critical situations.