Demonstrate an understanding of the background and context of the project. Justify the approach you used. Describe the equipment and/or software tools
used. describe the main outcomes and place them in context.
Research attempting to identify people at risk for AF have focussed on finding features in the electrical signal of the heart (measured through an
electrocardiogram or ECG) that can predict the likelihood of an individual suffering from AF in the future. These works have low sensitivity, possibly due to
only paying attention to specific segments of the ECG, ignoring most of the data that is collected. This project will develop a machine learning algorithm to
predict AF onset using the entire ECG signal‘s features and compare its sensitivity to current state-of-the art prediction algorithms.
The post Predicting atrial fibrillation (AF) onset using machine learning algorithms in python first appeared on COMPLIANT PAPERS.