Research on orthopedic auxiliary classification and prediction model based on XGBoost algorithm

In the big data environment, hospital medical data are also becoming more complex and diversified. The traditional method of manually processing data has not been able to meet the management needs of massive medical data. With the further development of big data technology and machine learning, the smart medical aided diagnosis model came into being. However, there is almost no auxiliary diagnosis mode for orthopedic diseases. In order to make up for the gap in the auxiliary diagnosis of orthopedics and promote the wisdom process of orthopedic disease diagnosis, this paper proposes an orthopedic auxiliary classification prediction model based on XGBoost algorithm. The experimental data were obtained from the clinical case information of femoral neck patients from April 2016 to October 2018, Department of Bone and Soft Tissue Tumor Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute. In order to make the experimental results more convincing, while constructing the XGBoost model, the orthopedic auxiliary classification prediction model is constructed based on the random forest algorithm and the associated classification algorithm, respectively, and the three models are compared and analyzed. The results show that compared with the random forest model and the associated classification model, the XGBoost algorithm classification prediction model has higher accuracy, faster calculation speed, and more applicability in orthopedic clinical data. The XGBoost algorithm can cope with complex and diverse medical data, and can better meet the requirements of timeliness and accuracy of auxiliary diagnosis. The classification and prediction model of orthopedic auxiliary diagnosis proposed in this paper helps to reduce the workload of medical workers, help patients prevent and recover early, and realize real auxiliary medical services.