Abstract: Objective To establish a prediction model for cardiotoxicity of Chinese herbal medicines (CHMs) in order to provide data for safety evaluation and rational clinical use of CHMs. Methods Active ingredients with potential cardiac risks were identified from the FDA Adverse Event Reporting System (FAERS) database by using the proportional imbalance method. The data was randomly divided into a training set and a validation set. The Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors(KNN), and Extreme Gradient Boosting (XGBoost) were used for modeling and internal verification. The performance of the model was evaluated using such criteria as the area under the curve (AUC), accuracy, and precision. Active ingredients of CHMs with cardiotoxicity were retrieved from literature that was published from the inception to January 1, 2025. CHMs with possible cardiac risks were retrieved from the spontaneous reporting system database. The Traditional Chinese Medicine Systems Pharmacology (TCMSP) database was searched for the active ingredients. The constructed model was externally validated via these tests. Results The model with the best predictability was the KNN. The AUC was 0.684 for the training set and 0.718 for the validation set. Twenty-five chemical ingredients of CHMs with cardiotoxicity were selected based on literature while eleven suspected cardiotoxic CHMs were selected from the spontaneous reporting system database. After external validation, eighteen chemical ingredients and ten CHMs were predicted to have cardiac risks. Conclusion The overall prediction of the model is 80% accurate, so it can be used for predicting cardiotoxicity of chemical ingredients in CHMs.

Key words: Traditional Chinese Medicine, Cardiotoxicity, Machine Learning, Quantitative Structure-Activity Relationship, Paullina cupana Kunth, Modeling for Prediction