Drug-induced long QT syndrome (diLQTS) is a common adverse drug reaction characterized by rapid and erratic heart beats that may instigate fainting or seizures. The onset of diLQTS can lead to torsades de points (TdP), a specific form of abnormal heart rhythm that often leads to sudden cardiac arrest and death. This study aims to understand the genetic similarities between diLQTS and TdP to develop a clinical decision support system (CDSS) to aide physicians in the prevention of TdP. Highly accurate classification algorithms, including random forests, shrunken centroid, and diagonal linear discriminant analysis are considered to build a prediction model for TdP. With a feasible set of markers, we accurately predict TdP classifications with an accuracy above 90%. The methodology used in this study can be extended to dealing with other biomedical high-dimensional data.