A machine learning approach: a classification model for overdose

Background British Columbia is currently the global hot spot of opioid overdose death. Despite various efforts, the opioid overdose crisis has deepened since 2015 with a historic mortality emphasizing the urgent need for effective predictive tools to manage and mitigate risks. Methods This study utilized a comprehensive dataset from the BC Provincial Overdose Cohort, applying machine learning techniques to predict the risk of fatal and general overdose. Data preprocessing included missing data handling through Multiple Imputation by Chained Equations (MICE) and class balance achieved via various resampling techniques. Models such as XGBoost, Random Forest, and deep learning were evaluated using 10-fold cross-validation, with performance metrics including accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic (AUROC). Results The Random Forest model achieved an accuracy of 88.77% and an AUROC of 91.12% in predicting fatal overdoses, while the XGBoost model reported an accuracy of 85.69% and an AUROC of 91.10% for general overdose predictions. Resampling techniques effectively addressed the class imbalance, enhancing the models' predictive accuracy significantly. Discussion The integration of ML models, particularly Random Forest and XGBoost, into comprehensive healthcare strategies offers a promising solution to the opioid crisis in British Columbia. Conclusion Effective overdose risk prediction necessitates quality data collection on overdose incidents. Implementing a real-time, accessible learning system for drug users to assess their risk could markedly improve intervention strategies.