Title: Preoperative staging of endometrial cancer based on decision tree model

Authors: Jun Xu; Hao Zeng; Shuqian He; Lingling Qin; Zhengjie Deng

Addresses: School of Information Science and Technology, Hainan Normal University, Haikou, Hainan, China ' School of Information Science and Technology, Hainan Normal University, Haikou, Hainan, China ' School of Information Science and Technology, Hainan Normal University, Haikou, Hainan, China ' Department of Ultrasound, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan, China ' School of Information Science and Technology, Hainan Normal University, Haikou, Hainan, China

Abstract: Endometrial cancer is extremely common in gynaecological tumours. Ultrasound technology has become an important detection method for endometrial cancer, but the accuracy of ultrasound diagnosis is not high. Therefore, using data-driven methods to accurately predict the preoperative staging of endometrial cancer has important clinical significance. To build a more accurate diagnosis model, this paper uses a decision tree model to analyse the preoperative staging diagnosis indicators of endometrial cancer. Experimental results show that the three-detection data of Tumour-Free Distance (TFD), ca125 and uterine to endometrial volume ratio are of high value for the diagnosis of endometrial cancer. The accuracy, sensitivity and specificity of the Random Forest (RF) model based on decision tree for preoperative staging of endometrial cancer were 97.71%, 94.11% and 100.00%, respectively. The comprehensive predictive ability based on the RF model has good application value for the prediction of preoperative staging of endometrial cancer.

Keywords: random forest; decision tree; machine learning; endometrial cancer; preoperative staging.

DOI: 10.1504/IJWMC.2026.151566

International Journal of Wireless and Mobile Computing, 2026 Vol.30 No.2, pp.207 - 215

Received: 25 Sep 2021
Received in revised form: 22 Mar 2022
Accepted: 04 Feb 2023
Published online: 09 Feb 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article