Back to Search Start Over

Prediction of Endometrial Carcinoma Using the Combination of Electronic Health Records and an Ensemble Machine Learning Method

Authors :
Wenwen Wang
Yang Xu
Suzhen Yuan
Zhiying Li
Xin Zhu
Qin Zhou
Wenfeng Shen
Shixuan Wang
Source :
Frontiers in Medicine, Vol 9 (2022)
Publication Year :
2022
Publisher :
Frontiers Media S.A., 2022.

Abstract

Endometrial carcinoma (EC) is a common cause of cancer death in women, and having an early accurate prediction model to identify this disease is crucial. The aim of this study was to develop a new machine learning (ML) model-based diagnostic prediction model for EC. We collected data from consecutive patients between November 2012 and January 2021 at tertiary hospitals in central China. Inclusion criteria included women undergoing endometrial biopsy, dilation and curettage, or hysterectomy. A total of 9 features, including patient demographics, vital signs, and laboratory and ultrasound results, were selected in the final analysis. This new model was combined with three top optimal ML methods, namely, logistic regression, gradient-boosted decision tree, and random forest. A total of 1,922 patients were eligible for final analysis and modeling. The ensemble model, called TJHPEC, was validated in an internal validation cohort and two external validation cohorts. The results showed that the AUC values were 0.9346, 0.8341, and 0.8649 for the prediction of total EC and 0.9347, 0.8073, and 0.871 for prediction of stage I EC. Nine clinical features were confirmed to be highly related to the prediction of EC in TJHPEC. In conclusion, our new model may be accurate for identifying EC, especially in the early stage, in the general population of central China.

Details

Language :
English
ISSN :
2296858X
Volume :
9
Database :
Directory of Open Access Journals
Journal :
Frontiers in Medicine
Publication Type :
Academic Journal
Accession number :
edsdoj.90edb18ab8de4e45a07e454a92a8c50a
Document Type :
article
Full Text :
https://doi.org/10.3389/fmed.2022.851890