This work presents a geometric study of computational disease tagging of ECGs problems. Using ideas like Earthmover’s distance (EMD) and Euclidean distance, it clusters category 1 and category −1 ECGs in two clusters, computes their average and then predicts the category of 100 test ECGs, if they belong to category 1 or category −1. We report 80% success rate using Euclidean distance at the cost of intense computation investment and 69% success using EMD. We suggest further ways to augment and enhance this automated classification scheme using bio-markers like Troponin isoforms, CKMB, BNP. Future direc- tions include study of larger sets of ECGs from diverse populations and collected from a heterogeneous mix of patients with different CVD conditions. Further we advocate the robustness of this programmatic approach as compared to deep learning kind of schemes which are amenable to dynamic instabilities. This work is a part of our ongoing framework Heart Regulated Intelligent Decision Assisted Information (HRIDAI) system.