Application of machine learning to predict employment attainment among individuals with intellectual and developmental disabilities.

Promoting desirable employment outcomes for individuals with intellectual and developmental disabilities has been an important task for decades. However, the statistics indicate inequitable employment outcomes still exist; including underrepresentation in the workforce and employment in a part-time, low-wage, and segregated setting. One way to address the gap is to review and promote individual and environmental characteristics that are related to enhanced employment outcomes. For this study, we used machine learning approaches to investigate the predictors of employment status in individuals with intellectual and developmental disabilities based on a national database in South Korea. All machine learning models employed in this study-specifically a Random Forest-accurately and consistently predicted employment outcomes for individuals with intellectual and developmental disabilities. The most important factors contributing to the model's predictive accuracy include employment capability, family support for employment, age, overall work ability, and daily living skills. Implications for practice and research are also discussed.