Abstract
Breast cancer is one of the malignant tumors having high incidence in women, the incidence of breast cancer has increased in all parts of the world since twentieth century, but its etiology is not yet completely clear, so it is very important to detect breast cells. In this paper, we built a regression model to detect breast cells, and generated a method for predicting the formation of benign and malignant breast cells by training the model, then we used the 10 features of breast cells to predict it, the results reaching upto 93.67% accuracy, it was very effective to predict and analyse whether the breast cells getting cancer, It had an important role in the diagnosis and prevention of breast cancer.
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Acknowledgments
This study was supported by the National Natural Science Foundation of China (61170192, 41271292), China Postdoctoral Science Foundation (No. 2015M580765), Chongqing Postdoctoral Science Foundation (No. Xm2016041), Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJ1603109, KJ1503207), the Fundamental Research Funds for the Central Universities (XDJK2014C039, XDJK2016C045), Doctoral Fund of Southwestern University (swu1114033).
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Zhu, Y., Chen, S., Chen, C., Chen, L. (2017). Partial Least Squares (PLS) Methods for Abnormal Detection of Breast Cells. In: Zou, B., Li, M., Wang, H., Song, X., Xie, W., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 727. Springer, Singapore. https://doi.org/10.1007/978-981-10-6385-5_8
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DOI: https://doi.org/10.1007/978-981-10-6385-5_8
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