Predicting Liver Injury Risk from Chemical Properties and Drug Label Information Using Machine Learning Models
- Title
- Predicting Liver Injury Risk from Chemical Properties and Drug Label Information Using Machine Learning Models
- Creator
- Gogi, Vyshali J.; Balakrishna, R.; Mathew, Ann
- Description
- 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.
- Source
- 2025 2nd International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2025;
- Date
- 01-01-2025
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Drug-Induced Liver Injury (DILI); Machine Learning; Risk Assessment
- Coverage
- 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
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISBN: 979-833159610-1;
- Format
- online
- Language
- English
- Type
- Conference paper
Collection
Citation
Gogi, Vyshali J.; Balakrishna, R.; Mathew, Ann, “Predicting Liver Injury Risk from Chemical Properties and Drug Label Information Using Machine Learning Models,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25916.