Background: The elderly with a limited body or bedridden are prone to pressure injury, and the Braden scale is often used as a risk assessment tool. However, few studies have explained the relationship between risk factors and risk levels using machine learning methods from Braden clinical observation data. Additionally, nearly half of the elderly over 75 years old in China are men.

Purpose: This study aimed to establish a pressure injury risk prediction model for elderly male patients using a machine learning method based on hospital clinical data. It further analyses the importance of risk factors and risk levels.

Methods: This study’s Braden observation data were obtained from the electronic medical records of elderly male patients from 27 October 2019 to 1 November 2020 in the case hospital. Rough set theory was used to identify the perception patterns between risk factors and risk levels based on the data.

Results: The importance of rough set theory showed that sensory perception and nutrition are key risk factors for identifying elderly male inpatients. Therefore, nurses should pay special attention to the measurement scores of these two risk factors. Moreover, this method also revealed conditions/decision rules for different risk levels. Among elderly male inpatients at risk of severe pressure injury, 42% of the observation data showed that their physical condition is completely limited in sensory perception, possibly insufficient nutrition, friction and shearing problems, and bedridden activities.

Conclusion: This model can effectively identify the critical risk factors and decision rules for different risk levels for pressure injury in elderly male inpatients. This allows nurses to focus on patients at a high risk of possible pressure injury in the future without increasing their workload. This study also provides a way to solve the problem that the Braden scale shows insufficient predictive validity and poor accuracy in identifying patients with different pressure injury risk levels, so it cannot fully reflect patients’ characteristics.