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Original Research

Open Access

A random forest-neural network coupled model for predicting the recurrence location of uterine cancer

  • Fengchun Liu1,2,3,4,5
  • Xiangdong Huang2,3,4,5,*,
  • Jian Wang2,5
  • Liya Wang1,2,3,4,5
  • Jingguo Qu1,3,4,6
  • Dianbo Hua7

1Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, 063509 Tangshan, Hebei, China

2Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, 063509 Tangshan, Hebei, China

3The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, 063509 Tangshan, Hebei, China

4Tangshan Intelligent Industry and Image Processing Technology Innovation Center, North China University of Science and Technology, 063509 Tangshan, Hebei, China

5College of Science, North China University of Science and Technology, 063509 Tangshan, Hebei, China

6College of Qian An, North China University of Science and Technology, 064400 Tangshan, Hebei, China

7Beijing Sitairui Cancer Data Analysis Joint Laboratory, 101149 Beijing, China

DOI: 10.22514/ejgo.2022.059 Vol.43,Issue 6,December 2022 pp.61-68

Submitted: 05 June 2022 Accepted: 26 July 2022

Published: 15 December 2022

*Corresponding Author(s): Xiangdong Huang E-mail:


With increasing developments and progress, the status and influence of women in society have significantly improved, with more attention paid to women’s health. According to relevant statistics, uterine cancer is a highly-incident malignant tumor in females and urges more research to improve the survival rate of uterine cancer patients. In this study, we established a prediction model to determine the location of uterine cancer recurrence by combining random forest and neural network algorithms. Data of uterine cancer patients were collected from major hospitals, and professional doctors evaluated and graded the patients’ physical fitness indicators based on their experience, which were then used to construct the model and obtain the prediction results. Compared to traditional method, the proposed method of this paper showed that the model was more effective and accurate in predicting the location of uterine cancer recurrence, with a prediction accuracy rate of up to 88.63%.


Uterine cancer; Random forest; Neural network; Coupled model

Cite and Share

Fengchun Liu,Xiangdong Huang,Jian Wang,Liya Wang,Jingguo Qu,Dianbo Hua. A random forest-neural network coupled model for predicting the recurrence location of uterine cancer. European Journal of Gynaecological Oncology. 2022. 43(6);61-68.


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