Harnessing Ensemble Machine Learning Models for Timely Diagnosis of Breast Cancer Metastasis: A Case Study on CatBoost, XGBoost, and LGBM › Research Explorer

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Harnessing Ensemble Machine Learning Models for Timely Diagnosis of Breast Cancer Metastasis: A Case Study on CatBoost, XGBoost, and LGBM

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

DOI

https://doi.org/10.1109/EDM61683.2024.10615210
Final published version

Minh Sao Khue Luu
Santanu Banerjee
Evgeniy N. Pavlovskiy
Bair N. Tuchinov

Original language	English
Title of host publication	International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM
Publisher	IEEE Computer Society
Pages	2320-2325
Number of pages	6
ISBN (Print)	9798350389234
DOIs	https://doi.org/10.1109/EDM61683.2024.10615210
Publication status	Published - 2024
Event	25th IEEE International Conference of Young Professionals in Electron Devices and Materials - Республика Алтай, Russian Federation Duration: 28 Jun 2024 → 2 Jul 2024 Conference number: 25 https://edm.ieeesiberia.org/

Publication series

Name	International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM

Conference

Conference	25th IEEE International Conference of Young Professionals in Electron Devices and Materials
Abbreviated title	EDM 2024
Country/Territory	Russian Federation
Period	28.06.2024 → 02.07.2024
Internet address	https://edm.ieeesiberia.org/

OECD FOS+WOS

1.02 COMPUTER AND INFORMATION SCIENCES

Research areas

gradient boosting, health informatics, healthcare equity, machine learning, metastatic breast cancer

ID: 60550286