摘要：In the big data era, learning-based techniques have attracted more and more attentions in many industry areas. The sport injury prediction is one of the most critical issues in data analysis of soccer teams.However, learning-based methods have not been widely used due to the poor data quality and computational capacity. In this paper, we propose a learning-based model to forecast sport injuries according to the data from various information systems. We first reduce the attributes that have significant impact on the injury risk by using learning-based methods.Then, we provide an algorithm based on the random forest method to prevent the over-fitting problem. We have evaluated the proposed model with the real-world data. The experimental results show that our model works efficiently and achieves low error rates.
