Application of an improved LightGBM hybrid integration model combining gradient harmonization and Jacobian regularization for breast cancer diagnosis
Abstract Cancer, as a shocking disease, is one of the most common malignant tumors among women, posing a huge threat to the physical health and safety of women worldwide. With the continuous development of science and technology, more and more high and new technologies are involved in the diagnosis...
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| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-86014-x |
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| author | Xiaoyan Sun |
| author_facet | Xiaoyan Sun |
| author_sort | Xiaoyan Sun |
| collection | DOAJ |
| description | Abstract Cancer, as a shocking disease, is one of the most common malignant tumors among women, posing a huge threat to the physical health and safety of women worldwide. With the continuous development of science and technology, more and more high and new technologies are involved in the diagnosis and prediction of breast cancer. In recent years, intelligent medical assistants supported by data mining and machine learning algorithms have provided necessary support for doctors’ diagnosis. This study proposes an improved LightGBM hybrid integration model. Introducing gradient harmonic loss and cross entropy loss to enhance the model’s attention to minority classes in the dataset and alleviate the impact of data imbalance on diagnostic results. Designing whale optimization algorithm to improve LightGBM to achieve iterative optimization of hyperparameters, and enhance the overall performance of the model. Proposing Jacobian regularization method to denoise LightGBM to solve the problem of model sensitivity to noise. Developing the LightGBM hybrid integration model to ensure the accuracy and stability of model diagnosis on diverse and imbalanced datasets. The effectiveness of the proposed method has been comprehensively compared and verified through the dataset in the UCI machine learning repository, and the results show that the proposed method has achieved good diagnostic performance in all indicators. The hybrid integration model proposed in this paper can provide effective auxiliary support for doctors to diagnose breast cancer. |
| format | Article |
| id | doaj-art-8696a037c326413d8744365032d9f169 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-8696a037c326413d8744365032d9f1692025-01-26T12:23:53ZengNature PortfolioScientific Reports2045-23222025-01-0115111810.1038/s41598-025-86014-xApplication of an improved LightGBM hybrid integration model combining gradient harmonization and Jacobian regularization for breast cancer diagnosisXiaoyan Sun0Obstetrics and Gynecology, Jinan Maternity and Child Care HospitalAbstract Cancer, as a shocking disease, is one of the most common malignant tumors among women, posing a huge threat to the physical health and safety of women worldwide. With the continuous development of science and technology, more and more high and new technologies are involved in the diagnosis and prediction of breast cancer. In recent years, intelligent medical assistants supported by data mining and machine learning algorithms have provided necessary support for doctors’ diagnosis. This study proposes an improved LightGBM hybrid integration model. Introducing gradient harmonic loss and cross entropy loss to enhance the model’s attention to minority classes in the dataset and alleviate the impact of data imbalance on diagnostic results. Designing whale optimization algorithm to improve LightGBM to achieve iterative optimization of hyperparameters, and enhance the overall performance of the model. Proposing Jacobian regularization method to denoise LightGBM to solve the problem of model sensitivity to noise. Developing the LightGBM hybrid integration model to ensure the accuracy and stability of model diagnosis on diverse and imbalanced datasets. The effectiveness of the proposed method has been comprehensively compared and verified through the dataset in the UCI machine learning repository, and the results show that the proposed method has achieved good diagnostic performance in all indicators. The hybrid integration model proposed in this paper can provide effective auxiliary support for doctors to diagnose breast cancer.https://doi.org/10.1038/s41598-025-86014-xBreast cancer diagnosisWhale optimization algorithm (WOA)Light gradient boosting machine (LightGBM)Hybrid integrationData mining |
| spellingShingle | Xiaoyan Sun Application of an improved LightGBM hybrid integration model combining gradient harmonization and Jacobian regularization for breast cancer diagnosis Scientific Reports Breast cancer diagnosis Whale optimization algorithm (WOA) Light gradient boosting machine (LightGBM) Hybrid integration Data mining |
| title | Application of an improved LightGBM hybrid integration model combining gradient harmonization and Jacobian regularization for breast cancer diagnosis |
| title_full | Application of an improved LightGBM hybrid integration model combining gradient harmonization and Jacobian regularization for breast cancer diagnosis |
| title_fullStr | Application of an improved LightGBM hybrid integration model combining gradient harmonization and Jacobian regularization for breast cancer diagnosis |
| title_full_unstemmed | Application of an improved LightGBM hybrid integration model combining gradient harmonization and Jacobian regularization for breast cancer diagnosis |
| title_short | Application of an improved LightGBM hybrid integration model combining gradient harmonization and Jacobian regularization for breast cancer diagnosis |
| title_sort | application of an improved lightgbm hybrid integration model combining gradient harmonization and jacobian regularization for breast cancer diagnosis |
| topic | Breast cancer diagnosis Whale optimization algorithm (WOA) Light gradient boosting machine (LightGBM) Hybrid integration Data mining |
| url | https://doi.org/10.1038/s41598-025-86014-x |
| work_keys_str_mv | AT xiaoyansun applicationofanimprovedlightgbmhybridintegrationmodelcombininggradientharmonizationandjacobianregularizationforbreastcancerdiagnosis |