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 Conference Paper Faculty Publications- Conference Papers 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 Gogi, Vyshali J.; Balakrishna, R.; Mathew, Ann Title A name given to the resource Predicting Liver Injury Risk from Chemical Properties and Drug Label Information Using Machine Learning Models 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 2025 2nd International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICCAMS65118.2025.11234050" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCAMS65118.2025.11234050</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105030029337?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030029337?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 Gogi V.J., Christ (Deemed to be University), Department of Statistics and Data Science, Bangalore, India; Balakrishna R., Christ (Deemed to be University), Department of Statistics and Data Science, Bangalore, India; Mathew A., Christ (Deemed to be University), Department of Statistics and Data Science, Bangalore, India Description An account of the resource This research aims to create a drug-induced liver injury (DILI) severity prediction system based on machine learning to aid healthcare professionals in safety assessment. FDA's Liver Toxicity Knowledge Base supplied a drug dataset of 1042 drugs, and later, after pre-processing and API data extraction, each drug was defined by 16 chemical features such as molecular descriptors and pharmacokinetic properties. To improve uniformity and get quality input for training, data preparation involved correcting missing values, encoding categorical values, and normalising numerical data. Various machine learning models were trained and evaluated to forecast the levels of DILI severity, i.e., Random Forest, Gradient Boosting, and XGBoost. The importance of features was approximated for identifying the predictors that impacted the most. The best overall performance was recorded for XGBoost, and it had 81% accuracy when it was evaluated. Its acceptable discrimination was established for mild, moderate, and severe cases. The aptness of being applied to the medical sector is demonstrated by drastically lowering the principal misclassifications, especially from mild to severe. The application of machine learning in improving medicine safety assessment and reducing risks associated with pharmaceutical development is illustrated here. 2025 IEEE. Subject The topic of the resource Drug-Induced Liver Injury (DILI); Machine Learning; Risk Assessment Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159610-1; Language A language of the resource English Type The nature or genre of the resource Conference paper 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