AI-enhanced approaches for personalized cardiac treatment: insights from ECG data.

The analysis of drug-induced alterations in the electrocardiogram (ECG) is essential in measuring cardiac safety, but manual analysis is not always accurate enough to identify subtle but important effects. This paper examines how machine learning (ML) models can be used to categorize various pharmacological treatments according to their distinct ECG patterns to establish a platform of individualized therapeutic evaluation. Using the public ECG Effects of Dofetilide, Moxifloxacin, Dofetilide+Mexiletine, Dofetilide+Lidocaine and Moxifloxacin+Diltiazem (ECGDMMLD) database, key electrophysiological features were extracted-including heart rate variability (HRV) and standard cardiac intervals (RR, PR, QT, QRS) to train and compare three different classifiers: XGBoost, Random Forest, and a Support Vector Machine (SVM). The analysis showed that tree-based ensemble techniques were very useful in this task. The XGBoost model had a better classification accuracy of 98.1%, which was closely followed by the random forest at 97.3%. Conversely, the SVM had much lower accuracy, implying that it was not as well adapted to the complexity of the high-dimensional ECG data. These results establish that ML models, particularly XGBoost, can accurately decode complex drug-induced cardiac signatures from ECG data. This work is a powerful demonstration of the proof-of-concept of automated and data-driven analytics integration into clinical processes to enhance drug safety and promote personalized medicine.