Adversarial training with GatedTabTransformer and the GAN to predict re-employment factors of Korean male workers after an industrial accident

Background: The re-employment of male workers after an industrial accident is a critical problem with substantial socioeconomic implications. This study proposes an advanced predictive model integrating the GatedTabTransformer with the generative adversarial network using adversarial training to improve the accuracy and robustness of re-employment predictions. Methods: We compared the performance of the proposed model against traditional machine learning techniques, including logistic regression, k-nearest neighbors, support vector machine, linear discriminant analysis, random forest, bagging, adaptive boosting and extreme gradient boosting on a dataset of 1383 male workers after an industrial accident. Results: The proposed model outperforms these traditional methods across the performance metrics, achieving an accuracy of 89.2%and an area under the receiver operating characteristic curve of 0.924. Furthermore, the analysis identified previous employment duration, age, injury severity, education level, and industry type as the most significant factors influencing re-employment. Conclusions: These findings underscore the potential of advanced machine learning techniques in addressing complex real-world problems and provide actionable insight for policymakers and practitioners focused on improving re-employment outcomes for male workers after an industrial accident.

Adversarial training; GatedTabTransformer; Generative adversarial network (GAN); Re-employment prediction; Korean worker; Industrial accident

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