Acute myocardial infarction is one of the most common cardiovascular diseases in the Western world. Fortunately, not all myocardial infarctions are fatal. By early diagnosis of acute myocardial infarction based on symptoms at a patient's presentation in the emergency department, the number of deaths may be further reduced, as life-saving actions can be taken sooner. In this paper, we investigate the application of kernel-based methods to this problem, i.e. we evaluate the performance of support vector machines and kernel logistic regression models and compare these two methods to logistic regression models in terms of discrimination and calibration. The results show that kernel-based methods have higher discriminatory power for early diagnosis of acute myocardial infarction than logistic regression models and that kernel logistic regression models have superior calibration in comparison to logistic regression models and support vector machines.