Keywords
Healthcare
How to Cite
Abstract
Adverse drug reactions (ADRs) pose significant challenges to patient safety and healthcare costs. Predicting ADRs using machine learning (ML) models can mitigate these risks by identifying potential reactions before widespread clinical use. This paper presents a comprehensive review of ML models for predicting ADRs, highlighting their strengths, limitations, and future directions. We discuss various data sources, feature selection techniques, and evaluation metrics used in ADR prediction. Additionally, we provide a comparative analysis of different ML algorithms and their performance in ADR prediction. Our findings indicate that ML models can effectively predict ADRs, but further research is needed to enhance their accuracy and generalizability.
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