Mutations in SCN5A can alter the cardiac sodium current INa and increase the risk of potentially lethal conditions such as Brugada and long-QT syndromes. The relation between mutations and their clinical phenotypes is complex, and systems to predict clinical severity of unclassified SCN5A variants perform poorly. We investigated if instead we could predict changes to INa, leaving the link from INa to clinical phenotype for mechanistic simulation studies. An exhaustive list of nonsynonymous missense mutations and resulting changes to INa was compiled. We then applied machine-learning methods to this dataset, and found that changes to INa could be predicted with higher sensitivity and specificity than most existing predictors of clinical significance. The substituted residues' location on the protein correlated with channel function and strongly contributed to predictions, while conservedness and physico-chemical properties did not. However, predictions were not sufficiently accurate to form a basis for mechanistic studies. These results show that changes to INa, the mechanism through which SCN5A mutations create cardiac risk, are already difficult to predict using purely in-silico methods. This partly explains the limited success of systems to predict clinical significance of SCN5A variants, and underscores the need for functional studies of INa in risk assessment.