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A novel electrophysiological cardiac model is introduced in this paper. The proposed cardiac model considers six key regions that characterize the cardiac electrical activity. This allows the model to solve the forward and inverse electrocardiology problems in near real time. The proposed cardiac model is used as a basis for two near real time clinical diagnostic applications. The first is the detection of myocardial ischemia. The second is the localization of myocardial infarction. These diagnostic methods use the proposed forward and inverse problem solutions and machine learning approaches to diagnose automatically, noninvasively, and accurately these two serious heart conditions. Moreover, the proposed diagnostic methods have high true positive and negative accuracies suitable to be used in clinical expert systems. The accuracies for the ischemia detection and infarction localization methods are 91% and 68.57%, respectively.