Comparing the performance of machine learning algorithms in classifying occupational noise-induced hearing loss

BackgroundPure-tone audiometry is the gold standard for diagnosing occupational noise-induced hearing loss (ONIHL); however, its reliance on expensive equipment, trained personnel, and subjective auditory responses limits its use in large-scale occupational screening.ObjectiveThis study aimed to develop and compare machine learning (ML) algorithms for ONIHL classification using audiometric data alone and audiometric data combined with demographic and clinical features, and to assess whether these variables provide additional predictive value beyond audiometric thresholds alone.MethodsIn this cross-sectional study of 1524 workers employed in the Iranian petrochemical industry, four ML algorithms, decision tree (DT), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost), were developed to classify ONIHL. Data were split 80:20 into training and testing sets. Model performance was evaluated using accuracy, precision, sensitivity, F1-score, and the kappa statistic.ResultsTree-based models outperformed SVM across ONIHL levels. Adding demographic-clinical variables improved accuracy, sensitivity, and F1-score by 2-5%. All models achieved 100% precision for normal hearing. For moderate-to-severe loss, XGBoost achieved 85% precision and 89% sensitivity, whereas RF achieved 81% and 87%, respectively. SVM performed less well for mild loss, likely because of sensitivity to class imbalance. The 6000 Hz threshold was the most important feature across models.ConclusionsML algorithms, particularly tree-based models, effectively identified ONIHL. Combining audiometric and demographic-clinical data enhanced performance and may support more accurate screening and timely intervention in high-risk occupational populations.