ItemArticle

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

Title
AI-enhanced approaches for personalized cardiac treatment: insights from ECG data
Creator
Tiwari, Vibha; Gupta, Rohit; Telang, Akshada; Tiwari, Akshra; Geddam, Rebakah; Awais, Muhammad; Khan, Muhammad Ahmed; Ghayvat, Hemant
Description
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 extractedincluding 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. The Author(s) 2026.
Source
npj Systems Biology and Applications;Volume;12;Issue;1;Article No.;72;
Date
01-01-2026
Publisher
Nature Research
Coverage
Tiwari V., Centre for Artificial Intelligence, Madhav Institute of Technology and Science (Deemed University), Madhya Pradesh, Gwalior, India, Department of Computer Science and Media Technology, Faculty of Technology, Linnaeus University, Vj Sweden; Gupta R., Centre for Artificial Intelligence, Madhav Institute of Technology and Science (Deemed University), Madhya Pradesh, Gwalior, India; Telang A., Centre for Artificial Intelligence, Madhav Institute of Technology and Science (Deemed University), Madhya Pradesh, Gwalior, India; Tiwari A., School of Sciences, Christ University, Uttar Pradesh, Ghaziabad, India; Geddam R., Department of Computer Science and Media Technology, Faculty of Technology, Linnaeus University, Vj Sweden, Unitedworld Institute of Technology, Karnavati University, Gujarat, Gandhinagar, India; Awais M., Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States; Khan M.A., Department of Electrical Engineering, Stanford University, Stanford, CA, United States; Ghayvat H., Department of Computer Science and Media Technology, Faculty of Technology, Linnaeus University, Vj Sweden, IMT, Department of Humanities and Technology, Roskilde University, Universitetsvej 1, Roskilde, Denmark
Rights
All Open Access; Gold Open Access; Green Open Access
Relation
ISSN: 20567189;
Format
online
Language
English
Type
Article

Citation

Tiwari, Vibha; Gupta, Rohit; Telang, Akshada; Tiwari, Akshra; Geddam, Rebakah; Awais, Muhammad; Khan, Muhammad Ahmed; Ghayvat, Hemant, “AI-enhanced approaches for personalized cardiac treatment: insights from ECG data,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/22517.

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