Survey of Machine Learning Techniques To Predict Heartbeat Arrhythmias
This work addresses the need for feasible real-time arrhythmia prediction in healthcare systems, but it is incremental as it focuses on comparing existing methods.
The paper compared various machine learning techniques to identify one that achieves high accuracy with low latency and memory overhead for real-time analysis of heartbeat arrhythmias from live hospital data.
Many works in biomedical computer science research use machine learning techniques to give accurate results. However, these techniques may not be feasible for real-time analysis of data pulled from live hospital feeds. In this project, different machine learning techniques are compared from various sources to find one that provides not only high accuracy but also low latency and memory overhead to be used in real-world health care systems.