Predicting Adverse Drug Reactions Using Machine Learning: A Hybrid Model Stacking Approach

Adverse drug reactions (ADRs) pose a significant challenge to healthcare systems worldwide. Accurate prediction of ADRs is necessary to prevent the patient from contracting unwanted side effects and improve vigilance within the pharmaceutical industry. This paper proposes a hybrid, ensemblebased machine learning model to predict the probability of adverse reactions based on drug-side effect relationships, patient indications, and co-occurrence patterns. The approach integrates extensive feature engineering, such as vectorization, calculation of side effect frequencies, indication-drug relevance scores, and co-occurrence metrics. Three state-of-the-art gradient boosting models, LightGBM, XGBoost, and CatBoost, were fine-tuned, trained, and evaluated on the engineered dataset. The predictions from each base model were stacked into a random forest regressor to build a hybrid meta-model. Upon evaluating the models using the mean absolute error as the primary metric alongside the mean squared error and the R² score as additional metrics, the stacked meta-model was found to have outperformed all base models, achieving a mean squared error of 0.000826, an R² score of 0.8789, and a mean absolute error of 0.01833. The obtained results highlight the potential of using hybrid models over individual machine learning systems whilst dealing with datasets that encompass deep, complex, non-linear relations and advanced feature engineering in predicting ADRs.