Article Data

  • Views 796
  • Dowloads 160

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.


[1] Xia C, Dong X, Li H, Cao M, Sun D, He S, et al. Cancer statistics in China and United States, 2022: profiles, trends, and determinants. Chinese Medical Journal. 2022; 135: 584–590.

[2] Senthilkumar G, Ramakrishnan J, Frnda J, Ramachandran M, Gupta D, Tiwari P, et al. Incorporating artificial fish swarm in ensemble classification framework for recurrence prediction of uterine cancer. IEEE Access. 2021; 9: 83876–83886.

[3] Guo C, Wang J, Wang Y, Qu X, Shi Z, Meng Y, et al. Novel artificial intelligence machine learning approaches to precisely predict survival and site-specific recurrence in uterine cancer: a multi-institutional study. Translational Oncology. 2021; 14: 101032.

[4] Cibula D, Dostálek L, Jarkovsky J, Mom CH, Lopez A, Falconer H, et al. The annual recurrence risk model for tailored surveillance strategy in patients with uterine cancer. European Journal of Cancer. 2021; 158: 111–122.

[5] Huo X, Zhou X, Peng P, Yu M, Zhang Y, Yang J, et al. Identification of a six-gene signature for predicting the overall survival of uterine cancer patients. OncoTargets and Therapy. 2021; 14: 809–822.

[6] Okubo M, Itonaga T, Saito T, Shiraishi S, Yunaiyama D, Mikami R, et al. Predicting factors for primary uterine cancer recurrence after definitive radiation therapy. BJR Open. 2021; 3: 20210050.

[7] Chai Z, Zhao C. Enhanced random forest with concurrent analysis of static and dynamic nodes for industrial fault classification. IEEE Transactions on Industrial Informatics. 2020; 16: 54–66.

[8] Tohry A, Chehreh Chelgani S, Matin SS, Noormohammadi M. Power-draw prediction by random forest based on operating parameters for an industrial ball mill. Advanced Powder Technology. 2020; 31: 967–972.

[9] Glock A. Explaining a random forest with the difference of two ARIMA models in an industrial fault detection scenario. Procedia Computer Science. 2021; 180: 476–481.

[10] Mylona E, Kourou K, Manikis G, Kondylakis H, Marias K, Karademas E, et al. ‘Prediction of poor mental health following breast cancer diagnosis using random forests’. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society. 31 October–04 November, 2021. IEEE. 2021.

[11] Bayramli I, Castro V, Barak-Corren Y, Madsen EM, Nock MK, Smoller JW, et al. Temporally informed random forests for suicide risk prediction. Journal of the American Medical Informatics Association. 2021; 29: 62–71.

[12] Yang R, Yu Y. Artificial convolutional neural network in object detection and semantic segmentation for medical imaging analysis. Frontiers in Oncology, 2021, 11: 573.

[13] Ziller A, Usynin D, Braren R, Makowski M, Rueckert D, Kaissis G. Medical imaging deep learning with differential privacy. Scientific Reports. 2021; 11: 13524.

[14] Aggarwal R, Sounderajah V, Martin G, Ting DSW, Karthikesalingam A, King D, et al. Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. Npj Digital Medicine. 2021; 4: 65.

[15] Alzubaidi L, Al-Amidie M, Al-Asadi A, Humaidi AJ, Al-Shamma O, Fadhel MA, et al. Novel transfer learning approach for medical imaging with limited labeled data. Cancers. 2021. 13: 1590.

[16] Izonin I, Tkachenko R, Dronyuk I, Tkachenko P, Gregus M, Rashkevych M. Predictive modeling based on small data in clinical medicine: RBF-based additive input-doubling method. Mathematical Biosciences and Engineering. 2021; 18: 2599–2613.

[17] Suzdaltseva M, Shamakhova A, Dobrenko N, Alekseeva O, Hammoud J, Gusarova N, et al. ‘De-identification of medical information for forming multimodal datasets to train neural networks’. Proceedings of the 7th International Conference on Information and Communication Technologies for Ageing Well and E-Health. Science and Technology: Russia. 2021.

Abstracted / indexed in

Science Citation Index Expanded (SciSearch) Created as SCI in 1964, Science Citation Index Expanded now indexes over 9,500 of the world’s most impactful journals across 178 scientific disciplines. More than 53 million records and 1.18 billion cited references date back from 1900 to present.

Biological Abstracts Easily discover critical journal coverage of the life sciences with Biological Abstracts, produced by the Web of Science Group, with topics ranging from botany to microbiology to pharmacology. Including BIOSIS indexing and MeSH terms, specialized indexing in Biological Abstracts helps you to discover more accurate, context-sensitive results.

Google Scholar Google Scholar is a freely accessible web search engine that indexes the full text or metadata of scholarly literature across an array of publishing formats and disciplines.

JournalSeek Genamics JournalSeek is the largest completely categorized database of freely available journal information available on the internet. The database presently contains 39226 titles. Journal information includes the description (aims and scope), journal abbreviation, journal homepage link, subject category and ISSN.

Current Contents - Clinical Medicine Current Contents - Clinical Medicine provides easy access to complete tables of contents, abstracts, bibliographic information and all other significant items in recently published issues from over 1,000 leading journals in clinical medicine.

BIOSIS Previews BIOSIS Previews is an English-language, bibliographic database service, with abstracts and citation indexing. It is part of Clarivate Analytics Web of Science suite. BIOSIS Previews indexes data from 1926 to the present.

Journal Citation Reports/Science Edition Journal Citation Reports/Science Edition aims to evaluate a journal’s value from multiple perspectives including the journal impact factor, descriptive data about a journal’s open access content as well as contributing authors, and provide readers a transparent and publisher-neutral data & statistics information about the journal.

Submission Turnaround Time