Prediction of Endometrial Carcinoma Recurrence Using a Stacking Ensemble with Meta-Learner

DOI

10.2991/978-94-6239-664-7_14How to use a DOI?

Keywords

Endometrial Carcinoma; XGBoost; LightGBM; CatBoost; Stacking Classifier; SMOTE; disesase-free survival

Abstract

Endometrial carcinoma is one of the most prevalent gynecological cancer in the world, recurrence of which impacts a lot on the survival of the patient. The conventional clinical markers are not usually able to render the interaction between the genomic and histopathological characteristics. In this research, we provide a state-of-the-art ensemble learning model that combines XGBoost, CatBoost, LightGBM, and multilayer perceptron based on stacking ensembles to predict the recurrence risk. For preprocessing these clinical data we have been used categorical encoding, feature scaling, and KNN imputation. The problem of class imbalance was solved using SMOTE. The performance was compared by use of F1-score, ROC-AUC and sensitivity analysis with the optimization of thresholds. Findings show that the stacking ensemble did better using standalone models, with a macro F1-score of 0.79 and ROC-AUC of 0.82, and better prognostic power than standalone models.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Khushnor Rahman Meem
AU  - Anamika Sanyal
AU  - Md Wakil Ahmed
AU  - Shahnewaj Limon
AU  - K. M. Nure Tanvir Siddique
PY  - 2026
DA  - 2026/06/08
TI  - Prediction of Endometrial Carcinoma Recurrence Using a Stacking Ensemble with Meta-Learner
BT  - Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
PB  - Atlantis Press
SP  - 177
EP  - 190
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-664-7_14
DO  - 10.2991/978-94-6239-664-7_14
ID  - Meem2026
ER  -