Develop and validate machine learning models to predict the risk of depressive symptoms in older adults with cognitive impairment

Machine learning models, particularly XGBoost, effectively predict depressive symptoms in cognitively impaired older adults. The findings highlight the importance of physical, cognitive, and social factors in depressive symptoms risk. These models have the potential to assist in early screening and intervention, improving patient outcomes. Future research should explore ways to enhance model generalizability, including the use of clinically diagnosed depressive symptoms data and alternative feature selection approaches.