A Novel Drug-Drug Indicator Dataset and Ensemble Stacking Model for Detection and Classification of Drug-Drug Interaction Indicators

Drug-drug interaction (DDI) is a significant public health issue that accounts for 30% of unanticipated clinically hazardous medication events. The past decade has seen an evolution in informatics-based research for DDI signal identification. This paper aims to create an ensemble stacking machine learning (ML) approach capable of accurately predicting novel DDI hazard indicators. The stacking ensemble machine learning architecture for predicting the signals of drug-drug interactions is supported by one of the most reliable sources of pharmacological data, DrugBank. We scrap a large dataset that contains drug-related information, including drug types, drug names, and other aspects of drug indicators, and make it publicly available to the research community. The proposed approach includes data preprocessing, balancing through a random oversampling, label encoding and one hot encoding technique used for categorical variables, and model prediction using an ensemble stacking technique. The proposed ensemble approach uses the Gradient Boosting (GB) classifier, Adaboost, and Gaussian Naive Bayes (GNB) to classify the drug indication types. The experimental results demonstrate that the suggested approach outperforms traditional machine learning approaches regarding accuracy and efficiency. The stacking model obtained the highest accuracy of 99.0%. The test indicates that the proposed model is more effective than traditional methods for detecting drug-drug interaction signals.

Automated summary

This paper proposes an ensemble stacking machine learning approach for accurately predicting novel DDI hazard indicators. Experimental results show that the proposed method outperforms traditional machine learning approaches in terms of accuracy and efficiency.