While going through the research papers and articles related to heart diseases, we observed that the doctors to patient ratio is very low, specifically in developing countries like India. At remote locations and tribal regions the availability of doctors is low. So, to help the doctors and people we thought of developing a system for primary screening. This will help the people to decide how soon they should consult the doctors to prevent the severe heart problems. The IoT module collects the parametric and real-time data using the ECG sensor, heart beat sensor, temperature sensor etc. and send this data to Arduino for real-time processing and to the cloud server for further storage. The front-end provides the option to receive the data from the user if he or she already have the test reports. Now, the machine learning model connected with the front end, Arduino and cloud storage analyses the data and predict the heart health with an accuracy of about 97%. The system is efficient in performing the multi-class classification and predicting whether the heart is healthy, at low risk, moderate risk, high risk or diseased. Even a medical assistant can operate the device for data collection and finding the prediction. He/she can share this data and prediction to the cardiologist for further decision making. So, it will decrease the burden and saves the time of cardio-specialists. This will increase the profit for hospitals. Also, the hospitals can provide low-cost diagnosis to the patients.

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