Application of statistics and machine learning for risk stratification of heritable cardiac arrhythmias

In the clinical management of heritable cardiac arrhythmias (HCAs), risk stratification is of prime importance. The ability to predict the likelihood of individuals within a sub-population contracting a pathology potentially resulting in sudden death gives subjects the opportunity to put preventive measures in place, and make the necessary lifestyle adjustments to increase their chances of survival. In this paper, we review classical methods that have commonly been used in clinical studies for risk stratification in HCA, such as odds ratios, hazard ratios, Chi-squared tests, and logistic regression, discussing their benefits and shortcomings. We then explore less common and more recent statistical and machine learning methods adopted by other biological studies and assess their applicability in the study of HCA. These methods typically support the multivariate analysis of risk factors, such as decision trees, neural networks, support vector machines and Bayesian classifiers. They have been adopted for feature selection of predictor variables in risk stratification studies, and in some cases, prove better than classical methods.