TY - GEN

T1 - Harnessing Ensemble Machine Learning Models for Timely Diagnosis of Breast Cancer Metastasis: A Case Study on CatBoost, XGBoost, and LGBM

AU - Luu, Minh Sao Khue

AU - Banerjee, Santanu

AU - Pavlovskiy, Evgeniy N.

AU - Tuchinov, Bair N.

N1 - Conference code: 25

PY - 2024

Y1 - 2024

N2 - This study employs three advanced gradient boosting machine learning algorithms to assess potential disparities in healthcare delivery. We specifically investigate which factors contribute to a patient's timely diagnosis of metastatic breast cancer using a public healthcare dataset. Our approach involves training and testing three separate models, as well as an ensemble model with automatically optimized weights. The models try to predict whether patients received a diagnosis of metastatic breast cancer within 90 days. Each model has different preprocessing and feature selection steps. The hyperparameter optimization is performed using the Optuna library in Python. Models are evaluated on the Kaggle platform, with our metrics indicating strong predictive performance; Categorical Boosting achieved an Area Under the Receiver Operating Characteristic Curve score of 0.813, Extreme Gradient Boosting reached 0.808, Light Gradient Boosting Machine scored 0.805, and the ensemble model culminated at 0.808. Additionally, we analyze the set of features being used by all the best models and examine the impact of hyperparameters on the models' overall performance.

AB - This study employs three advanced gradient boosting machine learning algorithms to assess potential disparities in healthcare delivery. We specifically investigate which factors contribute to a patient's timely diagnosis of metastatic breast cancer using a public healthcare dataset. Our approach involves training and testing three separate models, as well as an ensemble model with automatically optimized weights. The models try to predict whether patients received a diagnosis of metastatic breast cancer within 90 days. Each model has different preprocessing and feature selection steps. The hyperparameter optimization is performed using the Optuna library in Python. Models are evaluated on the Kaggle platform, with our metrics indicating strong predictive performance; Categorical Boosting achieved an Area Under the Receiver Operating Characteristic Curve score of 0.813, Extreme Gradient Boosting reached 0.808, Light Gradient Boosting Machine scored 0.805, and the ensemble model culminated at 0.808. Additionally, we analyze the set of features being used by all the best models and examine the impact of hyperparameters on the models' overall performance.

KW - gradient boosting

KW - health informatics

KW - healthcare equity

KW - machine learning

KW - metastatic breast cancer

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85201931798&origin=inward&txGid=8455f75d351cd53e743da6862370ab01

UR - https://www.mendeley.com/catalogue/0a3e884f-793a-34e4-ae20-216048a0db66/

U2 - 10.1109/EDM61683.2024.10615210

DO - 10.1109/EDM61683.2024.10615210

M3 - Conference contribution

SN - 9798350389234

T3 - International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM

SP - 2320

EP - 2325

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

PB - IEEE Computer Society

T2 - 25th IEEE International Conference of Young Professionals in Electron Devices and Materials

Y2 - 28 June 2024 through 2 July 2024

ER -