Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Upreti, Kamal; George, Jossy P.; Malik, Khushboo; Radhakrishnan, G. V.; Ga-B?aszczykowska, Agnieszka Title A name given to the resource AI-Enabled Early Detection of Chemo-Induced Cardiotoxicity Patterns Using ECG Time Series Data Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived American Journal of Clinical Oncology: Cancer Clinical Trials; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1097/COC.0000000000001280" target="_blank" rel="noreferrer noopener">https://doi.org/10.1097/COC.0000000000001280</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105029467813?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105029467813?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Upreti K., Department of Computer Science, Christ University, Delhi NCR, Uttar Pradesh, Ghaziabad, India; George J.P., Department of Computer Science, Christ University, Delhi NCR, Uttar Pradesh, Ghaziabad, India; Malik K., Department of Computer Science, Christ University, Delhi NCR, Uttar Pradesh, Ghaziabad, India; Radhakrishnan G.V., Department of Computer Science, KIIT School of Management (KSOM), Bhubaneswar, India; Ga-B?aszczykowska A., School of Law, War Studies University, Warsaw, Poland Description An account of the resource Objectives: Chemotherapy-induced cardiotoxicity is still a major clinical problem, usually appearing subclinically before structural or symptomatic cardiac dysfunction appears. Standard surveillance methods use imaging and biomarkers, which are time-intensive and money-intensive and can only identify damage at more advanced levels. Electrocardiography (ECG) provides a low-cost, non-invasive method that can detect early electrophysiological changes but is not fully utilized in cardio-oncology. The present work was designed to build an explainable machine learning model for predicting chemo-like cardiotoxicity patterns at an early stage from single-lead ECG signals. Methods: A public ECG data set (n=4997 segments) underwent preprocessing and was converted to 18 temporal, morphologic, and spectral features. Two ensemble learning algorithmsRandom Forest and XGBoostwere trained and validated with stratified splits. Model performance was assessed with ROCAUC, PRAUC, and F1-score with 1000 bootstrap resampling. Feature interpretability was evaluated through permutation importance and SHAP analysis. Results: Both models scored near-perfect classification (ROCAUC and PRAUC&gt;0.99, F1-score ? 0.986). Spectral entropy, band3 (high-energy frequency), QT surrogate, and peak count were the top features ranking alongside early cardiotoxicity indicators like repolarization instability and autonomic imbalance. Conclusions: The feature-driven, interpretable ML architecture suggested here shows that single-lead ECG has the potential to be an affordable and clinically relevant tool for the early detection of chemotherapy-induced cardiotoxicity. The method provides a feasible route toward implementation in precision cardio-oncology, particularly in resource-poor or ambulatory environments. 2025 Subject The topic of the resource cardio-oncology; cardiotoxicity; chemotherapy; ECG; precision medicine Publisher An entity responsible for making the resource available Lippincott Williams and Wilkins Relation A related resource ISSN: 2773732; CODEN: AJCOD Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online