Breast Cancer Prediction Based on RF-SVM

Research Article
Open access

Breast Cancer Prediction Based on RF-SVM

Sijun Chen 1*
  • 1 Northwest A&F University    
  • *corresponding author 2251007017@nwafu.edu.cn
Published on 1 August 2023 | https://doi.org/10.54254/2755-2721/8/20230293
ACE Vol.8
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-63-8
ISBN (Online): 978-1-915371-64-5

Abstract

Breast cancer prediction is crucial in identifying women who may be at risk for developing the disease. By doing the prediction, doctors can make the rapid diagnosis. Additionally, breast cancer prediction can also help guide research efforts and inform public health policies aimed at reducing the incidence and mortality of breast cancer. SVM (Support Vector Machine)is a classic method in machine learning, Random Forest is also widely used but they all have some shortcomings. Random Forest don’t have high accuracy. So RF-SVM(Random Forest and Random Forest) is be chosen to do the prediction. The goal of this research is to train a model that can achieve high accuracy in a relatively short time. As for the result, it shows that RF-SVM has achieved a high accuracy(0.95), compared with other method although RF(Random Forest) has the highest accuracy(0.97), it has the lowest precision(0.95). Over all, RF-SVM has the best overall performance. After trial, traditional machine learning methods turns out to be more stable.

Keywords:

breast cancer prediction, RF-SVM, machine learning, SVM

Chen,S. (2023). Breast Cancer Prediction Based on RF-SVM. Applied and Computational Engineering,8,657-666.
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References

[1]. Li L, Wang Y, Lu N and Lin G Yi 2021 Breast cancer prediction based on mixed comparisons of multi-classification algorithms C. Theo. Appl. 38(10) 1503-10

[2]. Wang H Qing, Wei Z, Zhang T Peng, Yu J Yu, Zhang X and Mu Y Xiang 2022 Study of a breast cancer prediction model based on the random forest algorithm C. Medi. Equipment. 19(01) 119-123

[3]. Wang D Guang and Huang Y Duo 2022 Breast cancer prediction based on the SVM-MLP Micro. Appl. 38(01) 130-3 138

[4]. Li F Xiang, Wang J Min, Liang J Chuang and Wang X 2022 Optimization of the naive Bayesian classification algorithm for discrete properties Micro.sys. 43(05) 897-901

[5]. Zhang W, Yu Y and Yang D 2001 Support vector machines for breast cancer diagnosis using mammographic features T. Medi. Imaging 20(11) 1082-89

[6]. Xu Y, Shi J and Xu L 2010 Computer-aided diagnosis of breast cancer using a data-driven Bayesian belief rule T. Medi. Imaging 29(2) 273-282

[7]. Kaggle. Breast Cancer Dataset, [online] Available: https://www.kaggle.com/datasets/nancyalaswad90/breast-cancer-dataset

[8]. Tang Y et al. DDoS attack detection method based on RF-SVM 2023 Software. Guide. (1-6)

[9]. Zhang H, Zhu J Peng, Zhuo D Cai and Xiang Y Study on concrete compressive strength prediction model based on random forest and support vector machine 2022 Engineering. Construction. 36(06) 1784-88 1815

[10]. Zhang L Shan, Yuan F Yin, Hu Y, Li T Jun, Wu X Guo and Yang S Study on freezing resistance of a tunnel based on random forest 2020 Construction. Tech. 49(17) 95-9

Cite this article

Chen,S. (2023). Breast Cancer Prediction Based on RF-SVM. Applied and Computational Engineering,8,657-666.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Software Engineering and Machine Learning

ISBN：978-1-915371-63-8(Print) / 978-1-915371-64-5(Online)
Editor：Anil Fernando, Marwan Omar
Conference website: http://www.confseml.org
Conference date: 19 April 2023
Series: Applied and Computational Engineering
Volume number: Vol.8
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Li L, Wang Y, Lu N and Lin G Yi 2021 Breast cancer prediction based on mixed comparisons of multi-classification algorithms C. Theo. Appl. 38(10) 1503-10

[2]. Wang H Qing, Wei Z, Zhang T Peng, Yu J Yu, Zhang X and Mu Y Xiang 2022 Study of a breast cancer prediction model based on the random forest algorithm C. Medi. Equipment. 19(01) 119-123

[3]. Wang D Guang and Huang Y Duo 2022 Breast cancer prediction based on the SVM-MLP Micro. Appl. 38(01) 130-3 138

[4]. Li F Xiang, Wang J Min, Liang J Chuang and Wang X 2022 Optimization of the naive Bayesian classification algorithm for discrete properties Micro.sys. 43(05) 897-901

[5]. Zhang W, Yu Y and Yang D 2001 Support vector machines for breast cancer diagnosis using mammographic features T. Medi. Imaging 20(11) 1082-89

[6]. Xu Y, Shi J and Xu L 2010 Computer-aided diagnosis of breast cancer using a data-driven Bayesian belief rule T. Medi. Imaging 29(2) 273-282

[7]. Kaggle. Breast Cancer Dataset, [online] Available: https://www.kaggle.com/datasets/nancyalaswad90/breast-cancer-dataset

[8]. Tang Y et al. DDoS attack detection method based on RF-SVM 2023 Software. Guide. (1-6)

[9]. Zhang H, Zhu J Peng, Zhuo D Cai and Xiang Y Study on concrete compressive strength prediction model based on random forest and support vector machine 2022 Engineering. Construction. 36(06) 1784-88 1815

[10]. Zhang L Shan, Yuan F Yin, Hu Y, Li T Jun, Wu X Guo and Yang S Study on freezing resistance of a tunnel based on random forest 2020 Construction. Tech. 49(17) 95-9

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