Musculoskeletal Disorder Prediction Using a Super Learner Ensemble Model on Ergonomic Workstation Data
Keywords:
Ensemble learning, Ergonomic workstation, Health risk prediction, Machine learning, Occupational health, Super learner, XGBoostAbstract
Musculoskeletal Disorders (MSDs) are common workplace injuries for those who work at computers in offices with poor ergonomics. This research examines Machine Learning (ML) approaches to predict MSD risk by using a synthetic ergonomics dataset in conjunction with a real-world dataset obtained from the Mendeley Data repository. This study examined six ML models: Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGBOOST). Then, the dataset was cleaned up, and utilized accuracy, precision, recall, and F1-score were used to evaluate the performance of the models. The results from ensemble approaches, notably RF, LightGBM, and XGBoost, performed better than traditional models. The accuracies were 86%, 88%, and 90%, respectively. A super learner ensemble model combining RF, LightGBM, and XGBoost, with Logistic Regression as the meta-learner, achieved an accuracy of 93% on the real MSD dataset and 95% on the synthetic dataset. This means that the Super Learner demonstrated superior predictive performance overall, with statistically significant improvements over DT, KNN, SVM, and RF, although its performance was not significantly different from LightGBM and XGBoost.
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