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PurposeCentral to the entire discipline of construction safety management is the concept of construction accidents. Although distinctive progress has been made in safety management applications over the last decades, construction industry still accounts for a considerable percentage of all workplace fatalities across the world. This study aims to predict occupational accident outcomes based on national data using machine learning (ML) methods coupled with several resampling strategies.Design/methodology/approachOccupational accident dataset recorded in Turkey was collected. To deal with the class imbalance issue between the number of nonfatal and fatal accidents, the dataset was pre-processed with random under-sampling (RUS), random over-sampling (ROS) and synthetic minority over-sampling technique (SMOTE). In addition, random forest (RF), Naïve Bayes (NB), K-Nearest neighbor (KNN) and artificial neural networks (ANNs) were employed as ML methods to predict accident outcomes.FindingsThe results highlighted that the RF outperformed other methods when the dataset was preprocessed with RUS. The permutation importance results obtained through the RF exhibited that the number of past accidents in the company, worker's age, material used, number of workers in the company, accident year, and time of the accident were the most significant attributes.Practical implicationsThe proposed framework can be used in construction sites on a monthly-basis to detect workers who have a high probability to experience fatal accidents, which can be a valuable decision-making input for safety professionals to reduce the number of fatal accidents.Social implicationsPractitioners and occupational health and safety (OHS) departments of construction firms can focus on the most important attributes identified by analysis results to enhance the workers' quality of life and well-being.Originality/valueThe literature on accident outcome predictions is limited in terms of dealing with imbalanced dataset through integrated resampling techniques and ML methods in the construction safety domain. A novel utilization plan was proposed and enhanced by the analysis results.