Resampling and Hyperparameter Tuning for Optimizing Breast Cancer Prediction Using Light Gradient Boosting

Kartika Handayani, Erni Erni, Rangga Pebrianto, Ari Abdilah, Rifky Permana, Eni Pudjiarti

2023

Abstract

Breast cancer is the most frequently diagnosed cancer and the leading cause of death. The main cause of breast cancer is mainly related to patients who inherit genetic mutations in genes. Early diagnosis of breast cancer patients is very important to prevent the rapid development of breast cancer apart from the evolution of preventive procedures. A machine learning (ML) approach can be used for early diagnosis of breast cancer. In this study, testing was performed using the Wisconsin Diagnostic Breast Cancer Dataset, also known as WDBC (Diagnostics) which consists of 569 instances with no missing values and has one target class attribute, either benign (B) or malignant (M). Tests were carried out using the ROS, RUS, SMOTE, and SMOTE-Tomek resampling techniques to see the effect of overcoming unbalanced data. Then tested with Light Gradient Boosting and optimized to get the best results using hyperparameter tuning. The best results are obtained after tuning the hyperparameter with accuracy 99.12%, recall 99.12%, precisions 99.13%, f1-score 99.13% and AUC 0.988.

Paper Citation

in Harvard Style

Handayani K., Erni E., Pebrianto R., Abdilah A., Permana R. and Pudjiarti E. (2023). Resampling and Hyperparameter Tuning for Optimizing Breast Cancer Prediction Using Light Gradient Boosting. In Proceedings of the 3rd International Conference on Advanced Information Scientific Development - Volume 1: ICAISD; ISBN 978-989-758-678-1, SciTePress, pages 177-180. DOI: 10.5220/0012446100003848

in Bibtex Style

@conference{icaisd23,
author={Kartika Handayani and Erni Erni and Rangga Pebrianto and Ari Abdilah and Rifky Permana and Eni Pudjiarti},
title={Resampling and Hyperparameter Tuning for Optimizing Breast Cancer Prediction Using Light Gradient Boosting},
booktitle={Proceedings of the 3rd International Conference on Advanced Information Scientific Development - Volume 1: ICAISD},
year={2023},
pages={177-180},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012446100003848},
isbn={978-989-758-678-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Advanced Information Scientific Development - Volume 1: ICAISD
TI - Resampling and Hyperparameter Tuning for Optimizing Breast Cancer Prediction Using Light Gradient Boosting
SN - 978-989-758-678-1
AU - Handayani K.
AU - Erni E.
AU - Pebrianto R.
AU - Abdilah A.
AU - Permana R.
AU - Pudjiarti E.
PY - 2023
SP - 177
EP - 180
DO - 10.5220/0012446100003848
PB - SciTePress