Estimation of Cardiac and Non-cardiac Diagnosis from Electrocardiogram Features

Ensuring timely and accurate diagnosis of medical conditions is paramount for effective patient care.Electrocardiogram (ECG) signals are fundamental for evaluating a patient's cardiac health and are readily available.Despite this, little attention has been given to the remarkable potential of ECG data in detecting non-cardiac conditions.In our study, we used publicly available datasets (MIMIC-IV-ECG-ICD and ECG-VIEW II) to investigate the feasibility of inferring general diagnostic conditions from ECG features.To this end, we trained a tree-based model (XG-Boost) based on ECG features and basic demographic features to estimate a wide range of diagnoses, encompassing both cardiac and non-cardiac conditions.Our results demonstrate the reliability of estimating 23 cardiac as well as 21 non-cardiac conditions above 0.7 AUROC in a statistically significant manner across a wide range of physiological categories.Our findings underscore the predictive potential of ECG data in identifying well-known cardiac conditions.However, even more striking, this research represents a pioneering effort in systematically expanding the scope of ECG-based diagnosis to conditions not traditionally associated with the cardiac system.