Breast cancer diagnosis using feature extraction and boosted C5.0 decision tree algorithm with penalty factor

Jian-xue Tian; Jue Zhang; Jian-xue Tian; Jue Zhang

doi:10.3934/mbe.2022102

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 3: 2193-2205. doi: 10.3934/mbe.2022102

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Research article

Breast cancer diagnosis using feature extraction and boosted C5.0 decision tree algorithm with penalty factor

Jian-xue Tian ,
Jue Zhang ^,

School of Information Engineer, Yulin University, Road chongwen, Yulin 719000, China

Academic Editor: Xiao Wang

Received: 16 September 2021 Revised: 28 November 2021 Accepted: 14 December 2021 Published: 04 January 2022

To overcome the two class imbalance problem among breast cancer diagnosis, a hybrid method by combining principal component analysis (PCA) and boosted C5.0 decision tree algorithm with penalty factor is proposed to address this issue. PCA is used to reduce the dimension of feature subset. The boosted C5.0 decision tree algorithm is utilized as an ensemble classifier for classification. Penalty factor is used to optimize the classification result. To demonstrate the efficiency of the proposed method, it is implemented on biased-representative breast cancer datasets from the University of California Irvine(UCI) machine learning repository. Given the experimental results and further analysis, our proposal is a promising method for breast cancer and can be used as an alternative method in class imbalance learning. Indeed, we observe that the feature extraction process has helped us improve diagnostic accuracy. We also demonstrate that the extracted features considering breast cancer issues are essential to high diagnostic accuracy.
- breast cancer diagnosis,
- boosted C5.0 algorithm,
- principle component analysis
Citation: Jian-xue Tian, Jue Zhang. Breast cancer diagnosis using feature extraction and boosted C5.0 decision tree algorithm with penalty factor[J]. Mathematical Biosciences and Engineering, 2022, 19(3): 2193-2205. doi: 10.3934/mbe.2022102

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Abstract

To overcome the two class imbalance problem among breast cancer diagnosis, a hybrid method by combining principal component analysis (PCA) and boosted C5.0 decision tree algorithm with penalty factor is proposed to address this issue. PCA is used to reduce the dimension of feature subset. The boosted C5.0 decision tree algorithm is utilized as an ensemble classifier for classification. Penalty factor is used to optimize the classification result. To demonstrate the efficiency of the proposed method, it is implemented on biased-representative breast cancer datasets from the University of California Irvine(UCI) machine learning repository. Given the experimental results and further analysis, our proposal is a promising method for breast cancer and can be used as an alternative method in class imbalance learning. Indeed, we observe that the feature extraction process has helped us improve diagnostic accuracy. We also demonstrate that the extracted features considering breast cancer issues are essential to high diagnostic accuracy.

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