Early warning model of recurrence site for breast cancer rehabilitation patients based on adaptive mesh optimization XGBoost
1Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, 063210 Tangshan, Hebei, China
2Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, 063210 Tangshan, Hebei, China
3The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, 063210 Tangshan, Hebei, China
4Tangshan Intelligent Industry and Image Processing Technology Innovation Center, North China University of Science and Technology, 063210 Tangshan, Hebei, China
5College of Science, North China University of Science and Technology, 063210 Tangshan, Hebei, China
6School of Metallurgy and Energy, North China University of Science and Technology, 063210 Tangshan, Hebei, China
7Beijing Sitairui Cancer Data Analysis Joint Laboratory, 101100 Beijing, China
DOI: 10.22514/ejgo.2022.031 Vol.43,Issue 4,August 2022 pp.40-48
Submitted: 03 June 2022 Accepted: 12 July 2022
Published: 15 August 2022
Objective: Breast cancer is a malignant disease with a high mortality rate. Using postoperative rehabilitation data of breast cancer patients, this study explored the effects of immune, tumor, microenvironmental, psychological, nutritional, aerobic exercise and advanced work indexes on the rehabilitation of breast cancer patients. To determine the weight of the impact of different indications on cancer recovery. By combining the adaptive grid optimization algorithm with the XGBoost (Extreme Gradient Boosting) algorithm, an intelligent prediction model for breast cancer rehabilitation was constructed using patients indexes as input and the recurrence location as the output. Our results showed that the model constructed in this study could effectively predict cancer cell metastasis during breast cancer recurrence in recovered patients. Compared with artificial intelligence algorithm models such as neural network algorithm, support vector machine algorithm, gradient boosting tree algorithm and Adaboost, the model demonstrated a forecast accuracy rate of >93%. The model established in this study could effectively predict the recurrence position of breast cancer and provide an auxiliary reference for doctors to treat breast cancer patients more effectively.
Breast cancer; Adaptive mesh optimization; XGBoost; Location of recurrence
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