Predicting atrial fibrillation (AF) onset using machine learning algorithms in python

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 focused 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.

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