In this study we introduce a precise fall-risk screening method for large-scale older population. Based on a dataset including 7084 older adults across 30 provinces in China, we developed a data-driven method to identify the fall-risk group and determine the major characteristics in older adults. First, the entire sample were divided into two groups by gender based on analysis of Cluster Feature Tree. Extreme Gradient Boosting models confirmed that patient clustering can improve the performance of fall-risk prediction, and pinpointed the common and different important features for different patient groups. The findings provide evidence for future behavioral trait indicators for geriatric rehabilitation and have potential to enhance geriatric health management in primary care.