  Torsades de pointes (TdP) is an irregular heart rhythm as a side effect of
drugs and may cause sudden cardiac death. A machine learning model that can
accurately identify drug TdP risk is necessary. This study uses multinomial
logistic regression models to predict three-class drug TdP risks based on
datasets generated from rabbit ventricular wedge assay experiments. The
training-test split and five-fold cross-validation provide unbiased
measurements for prediction accuracy. We utilize bootstrap to construct a 95%
confidence interval for prediction accuracy. The model interpretation is
further demonstrated by permutation predictor importance. Our study offers an
interpretable modeling method suitable for drug TdP risk prediction. Our method
can be easily generalized to broader applications of drug side effect
assessment.
