Partial Least Squares (PLS) Methods for Abnormal Detection of Breast Cells

被引：1

作者：

Zhu, Yuchen ^{[1
]}

Chen, Shanxiong ^{[1
]}

Chen, Chunrong ^{[1
]}

Chen, Lin ^{[1
]}

机构：

[1] Southwest Univ, Sch Comp & Informat Sci, Chongqing 400715, Peoples R China

来源：

DATA SCIENCE, PT 1 | 2017年 / 727卷

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

Partial least squares; Multivariate analysis; Breast cancer; Prediction; MODEL;

D O I：

10.1007/978-981-10-6385-5_8

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：88 / 99

页数：12

共 13 条

[1] Developing a PLS model for determination of total phenolic content in aged cachacas [J].

Bernardes, Cristina D. ;

de Figueiredo, Meliza C. P. ;

Barbeira, Paulo J. S. .

MICROCHEMICAL JOURNAL, 2014, 116 :173-177

[2] A PLS regression model using flame spectroscopy emission for determination of octane numbers in gasoline [J].

de Paulo, Jaqueline M. ;

Barros, Jose E. M. ;

Barbeira, Paulo J. S. .

FUEL, 2016, 176 :216-221

[3] Anomaly intrusion detection based on PLS feature extraction and core vector machine [J].

Gan Xu-sheng ;

Duanmu Jing-shun ;

Wang Jia-fu ;

Cong Wei .

KNOWLEDGE-BASED SYSTEMS, 2013, 40 :1-6

[4] Three-way PLS regression and dual energy gamma densitometry for prediction of total volume fractions and enhanced flow regime identification in multiphase flow [J].

Halstensen, Maths ;

Amundsen, Lene ;

Arvoh, Benjamin K. .

FLOW MEASUREMENT AND INSTRUMENTATION, 2014, 40 :133-141

[5]

Ji S, 2009, CHIN BUS, V9, P138

[6] Partial Least Squares (PLS) methods for neuroimaging: A tutorial and review [J].

Krishnan, Anjali ;

Williams, Lynne J. ;

McIntosh, Anthony Randal ;

Abdi, Herve .

NEUROIMAGE, 2011, 56 (02) :455-475

[7]

Li J.F., 2005, CHIN J GENER PRACT, V4, P336

[8] Industrial PLS model variable selection using moving window variable importance in projection [J].

Lu, Bo ;

Castillo, Ivan ;

Chiang, Leo ;

Edgar, Thomas F. .

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2014, 135 :90-109

[9] Quality prediction and quality-relevant monitoring with multilinear PLS for batch processes [J].

Luo, Lijia ;

Bao, Shiyi ;

Mao, Jianfeng ;

Tang, Di .

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2016, 150 :9-22

[10]

Wang H., 2006, Linear and Nonlinear Methods of Partial Least Squares Regression

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