Innovative Research on Physical Data Management of Football Players Under the Background of Smart Medical Treatment

With the intensification of the competition for public medical resources and the rapid development of smart medical technology, the demand for in-depth analysis of athletes’ basic information and decision support is growing day by day. However, the practice of applying smart medicine to athletes’ physical information management is still relatively limited. This paper aims to explore how to use smart medical decision technology to build an integrated athlete physical information management and decision support platform. Taking football players as the research object, this paper integrates data mining technology into a smart medical decision-making model, to achieve accurate analysis and effective management of athletes’ physical fitness information. The core of the research is to develop a smart healthcare decision model that can identify key information and patterns on the platform through data mining technology. In the selection of the decision algorithm, this paper adopts cluster analysis and association rule mining algorithms, which can deeply dig into the potential rules and correlations in the data. Through the application of an algorithm, we can accurately evaluate the physical condition of football players, and provide scientific guidance and suggestions for their subsequent physical training. The algorithm in this study is not only innovative in theory, but also shows good adaptability and effectiveness in practical application.