Assessment of non-fatal injuries among university students in Hainan: a machine learning approach to exploring key factors.
Journal:
Frontiers in public health
PMID:
39639893
Abstract
BACKGROUND: Injuries constitute a significant global public health concern, particularly among individuals aged 0-34. These injuries are affected by various social, psychological, and physiological factors and are no longer viewed merely as accidental occurrences. Existing research has identified multiple risk factors for injuries; however, they often focus on the cases of children or the older adult, neglecting the university students. Machine learning (ML) can provide advanced analytics and is better suited to complex, nonlinear data compared to traditional methods. That said, ML has been underutilized in injury research despite its great potential. To fill this gap, this study applies ML to analyze injury data among university students in Hainan Province. The purpose is to provide insights into developing effective prevention strategies. To explore the relationship between scores on the self-rating anxiety scale and self-rating depression scale and the risk of non-fatal injuries within 1 year, we categorized these scores into two groups using restricted cubic splines.