IoT-Based Cardiac Arrest Prediction Through Heart Variability Analysis

Current machine learning methods for sudden cardiac arrest have not been tested against physically active heart rates. Developments in wearable technology and advancements in non-intrusive heart rate monitors may allow for a future where people can stream their heart rate readings, with the readings automatically analyzed by robust machine learning algorithms which will alert cardiac arrest risk. This paper presents a new sudden cardiac arrest prediction technique, a random forest classifier implementation, a prospective physical activity heart rate dataset, and an Internet of things solution toward heart rate monitoring and sudden cardiac arrest warning. In this paper, five minutes advance warning is provided with 97.03% accuracy and a 0.9485 F-score for the classification of sudden cardiac arrest prediction. The result shows the efficiency of our method compared to other existing methods.