Novel studies conducting cardiac safety assessment using human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are promising but might be limited by their specificity and predictivity. It is often challenging to correctly classify ionchannel blockers or to sufficiently predict the risk forTorsade de Pointes (TdP). In this study, we developed a method combining in vitro and in silico experiments to improve machine learning approaches in delivering fast and reliable prediction of drug-induced ion-channel blockade and proarrhythmic behaviour.The algorithm is based on the construction of a dictionary and a greedy optimization, leading to the definition of optimal classifiers. Finally,we present a numerical tool that can accurately predict compound-induced pro-arrhythmicrisk and involvement of sodium,calcium and potassium channels,based on hiPSC-CM field potentialdata.