Cardiovascular disease remains a most important public health issue, with the number of heart patients increasing due to inadequate health awareness and poor dietary habits. Therefore, there is a pressing need to develop techniques that can swiftly identify cardiac disease across large sample sizes. This study evaluates various approaches for their effectiveness in predicting cardiac disease. Using seven different classification performance indices and the (ROC) receiver operating characteristic curve, the methods were assessed for their predictive capability. Among the techniques tested, the random forest classification algorithm achieved the maximum accuracy, with a highest classification accuracy of 98%.