ItemConference Paper

Breast Cancer Prediction using a Stacked Ensemble of XGBoost and LightGBM with Logistic Regression Meta-Learning

Title
Breast Cancer Prediction using a Stacked Ensemble of XGBoost and LightGBM with Logistic Regression Meta-Learning
Creator
Joseph, Ankita; George, Jossy; Alapatt, Bosco Paul
Description
Breast cancer remains one of the major reasons for cancer deaths in women, which is why it is key to develop and improve diagnostic systems for accurate predictions. Currently, the advent of Machine learning has helped in providing powerful algorithms to achieve advancements in cancer detection. However, the main motivation of this research is to focus on building more complex ensemble architectures, as they are known for significantly improving predictive accuracy, robustness, and generalisation, especially in performing complex tasks such as medical diagnosis. In this research, a Hybrid stacking ensemble was built using two gradient boosting techniques, XGBoost and LightGBM, with a Logistic Regression meta-learner to predict breast cancer and compare their performance with standard classifiers. The Breast Cancer Wisconsin (Diagnostic) dataset, which consists of 569 patient records, was utilised for model training and analysis. The data was preprocessed using Z-score normalisation and stratified 5-fold cross-validation. The machine learning algorithms, such as Decision Tree, Logistic Regression, and Random Forest, were compared with the hybrid model, and the metrics used for comparison were accuracy, precision, recall, F1-score, and ROC-AUC. The proposed hybrid model performed well, achieving a high accuracy rate of 97.37% and a recall rate of 93.00% for malignant cases. McNemar's test (p > 0.05) confirms that this accuracy rate is statistically equivalent to the Random Forest classifier. These findings proved that the proposed model can perform optimally in predicting complex data with the same degree of precision as the standard models. Therefore, the hybrid model can be considered a robust and reliable new alternative for breast cancer prediction. 2026 IEEE.
Source
Proceedings of the 2026 International Conference on AI-Driven Smart Systems and Ubiquitous Computing, ICAUC 2026;pp.1366-1373
Date
01-01-2026
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Breast Cancer Prediction; Classifier; Hybrid Ensemble; LightGBM; Random Forest; Stacking; XGBoost
Coverage
Joseph A., Christ (Deemed to be University), Department of Computer Science, India; George J., Christ (Deemed to be University), Department of Computer Science, India; Alapatt B.P., Christ (Deemed to be University), Department of Computer Science, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833155851-2;
Format
online
Language
English
Type
Conference paper

Citation

Joseph, Ankita; George, Jossy; Alapatt, Bosco Paul, “Breast Cancer Prediction using a Stacked Ensemble of XGBoost and LightGBM with Logistic Regression Meta-Learning,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 20, 2026, https://archives.christuniversity.in/items/show/25904.

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