Long QT syndrome (LQTS) is a life-threatening genetic disorder characterized by prolonged QT intervals on electrocardiograms. Congenital forms are mostly associated with variants in the KCNQ1 and KCNH2 genes. Among pathogenic or likely pathogenic (P/LP) variants, some are associated with a significantly higher incidence of cardiac events compared to others. While therapies have significantly reduced mortality, some patients are unresponsive or intolerant to therapy, perpetuating their arrhythmic risk, including sudden cardiac death. Current approaches for risk stratification are insufficient, highlighting the critical need for more accurate identification and management of patients carrying high risk genetic variants.Here, we aimed to develop a refined risk stratification model for P/LP variants by applying machine learning classification to electrophysiological data measured in patient-specific human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs).