Universal Journal of Public Health Vol. 12(3), pp. 434 - 440
DOI: 10.13189/ujph.2024.120302
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Ensemble of XGBoost Classifiers Based on LDA Dimensionality Reduction for Predicting Breast Cancer

Mai Nhu Uyen Le ^1,2, Jianlin Zhou ¹, Dinh Phu Cuong Le ^3,4,*, Dong Wang ^3
1 State Key Laboratory of Developmental Biology of Freshwater Fish & Key Laboratory of Protein Chemistry and Developmental Biology of the Ministry of Education, College of Life Science, Hunan Normal University, China
² Faculty of Medicine and Pharmacy, Yersin University of Dalat, Vietnam
³ College of Computer Science and Electronic Engineering, Hunan University, China
⁴ Yersin University of Dalat, Vietnam

ABSTRACT

As reported by the World Health Organization, breast cancer is recognized as the most popular disease in women. Thus, the need for early and accurate detection of this cancer for effective treatment is highly demanded. In this paper, a novel machine learning-based method is proposed to improve the success of breast cancer prediction. To be specific, Extreme Gradient Boosting (XGBoost), which is an efficient machine learning algorithm to deal with large datasets, is applied with the help of the Linear Discriminant Analysis (LDA) algorithm, which is often used for dimensionality reduction by fusing the original multidimensional data features, to create the cancer predictive model. From the experimental results, with the LDA, it is shown that the XGBoost classifier can help to improve the classification accuracy by 2.7 % compared to the classifier without using LDA. Moreover, when compared to other machine learning methods, the proposed method also shows a better classification result with the root mean squared error of 0.115, which means that its error is at least 2.6 % lower than others. The proposed method aims to support doctors in enhancing clinical application as well as improving medical quality, especially when detecting the very first moment of breast cancer.

KEYWORDS
Breast Cancer, Machine Learning, XGBoost, Dimensionality Reduction, LDA

Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] Mai Nhu Uyen Le , Jianlin Zhou , Dinh Phu Cuong Le , Dong Wang , "Ensemble of XGBoost Classifiers Based on LDA Dimensionality Reduction for Predicting Breast Cancer," Universal Journal of Public Health, Vol. 12, No. 3, pp. 434 - 440, 2024. DOI: 10.13189/ujph.2024.120302.

(b). APA Format:
Mai Nhu Uyen Le , Jianlin Zhou , Dinh Phu Cuong Le , Dong Wang (2024). Ensemble of XGBoost Classifiers Based on LDA Dimensionality Reduction for Predicting Breast Cancer. Universal Journal of Public Health, 12(3), 434 - 440. DOI: 10.13189/ujph.2024.120302.