AUTHOR=Shah Apeksha , Ahirrao Swati , Pandya Sharnil , Kotecha Ketan , Rathod Suresh TITLE=Smart Cardiac Framework for an Early Detection of Cardiac Arrest Condition and Risk JOURNAL=Frontiers in Public Health VOLUME=Volume 9 - 2021 YEAR=2021 URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2021.762303 DOI=10.3389/fpubh.2021.762303 ISSN=2296-2565 ABSTRACT=Cardiovascular disease is considered to be one of the most epidemic diseases in the world today. Predicting cardiovascular diseases such as cardiac arrest is a difficult task in the area of healthcare. The Healthcare industry has a vast collection of datasets for analysis and prediction purposes. Somehow, the predictions made on these publicly available datasets may be erroneous. To make the prediction accurate, real-time data needs to be collected. This study collected real-time data using sensors and stored it on a cloud computing platform such as Google Firebase. The acquired data is then classified using six machine learning algorithms: Artificial Neural Network, Random Forest Classifier, Gradient Boost XGBoost classifier, Support Vector Machine, Naïve Bayes, and Decision Tree. Furthermore, we have presented two novel gender-based risk classification and age-wise risk classification approach in the undertaken study. The presented approaches have used Kaplan-Meier and Cox-regression survival analysis methodology for risk detection and classification. The presented approaches also assist health experts in identifying the risk probability risk and the 10-year risk score prediction. The proposed system is an economical alternative to the existing system due to its low cost. The outcome obtained shows an enhanced level in performance with an overall accuracy of 98% using Decision Tree on our collected dataset for cardiac risk prediction. We also introduced two risk classification models for Gender and Age wise people to detect their survival probability. The outcome of the proposed model shows accurate probability in both classes.