Cardiovascular Disease Diagnosing Using Machine Learning

Cardiovascular diseases (CVDs) are the primary cause of death, and hence, early identify and management would be absolutely important for saving lives. Heart rate monitoring device based on the Arduino platform using heart rate sensor, near infrared sensor and machine learning to provide accurate and reliable results. The data is analyzed by a machine learning model using the Bagging algorithm that gives predictive insights into cardiac health. A web-based dashboard is provided to show real-time analytic, and in case of abnormal readings, the GSM module sends SMS to the consulting doctor for further action. The system eliminates challenges that are associated with the traditional methods, such as high costs, delayed information, and the need for elaborate equipment and setup, thus suitable for home and remote monitoring. Through implementing the proposed system for the data alert collection system, analysis and accessibility of the data is improved, monitoring is made continuous, and the approach towards managing cardiovascular diseases is made more proactive.