maintaining AF. AF is driven through the spontaneous emergence of re-entrant circuits which generate rapid, irregular atrial activity. This mechanism is a form of micro-anatomical re-entry. These circuits form at regions with high levels of localised fibrosis. The model also explains the wide range of AF classifications from short intermittent episodes to long lasting permanent AF as increasing levels of fibrosis modify the myocardium [@manani2016]. In addition to the mechanistic benefits of cellular automata, their popularity has recently grown given their capacity to simulate much longer timescales of atrial activity than computationally intensive continuous models [@lin2017; @lord2013; @ciaccio2017b].

Machine learning approaches are statistical techniques implemented computationally which excel at finding hidden insights in complex data without being explicitly programmed to do so. In particular, machine learning has had recent successes in the medical community in classifying skin melanoma and in genetic sequencing [@Esteva2017; @Libbrecht2015]. These methods often rely on having a large dataset for the models to learn correlations from. In clinical medicine, the lack of good quality data in large quantities often makes such an approach difficult. However, when working with computer simulations such as those in the CMP model, data can be generated in sufficient quantities, and hence, 