Developing a Novel Machine Learning-Based Classification Scheme for Predicting SPCs in Breast Cancer Survivors

Chi-Chang Chang; Ssu-Han Chen

doi:10.3389/fgene.2019.00848

journal article Open Access Sep 18, 2019

Developing a Novel Machine Learning-Based Classification Scheme for Predicting SPCs in Breast Cancer Survivors

Chi-Chang Chang

Ssu-Han Chen

Frontiers in Genetics Vol. 10 · Frontiers Media SA

View at Publisher Save 10.3389/fgene.2019.00848

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References

35

[1]

Abreu "Predicting breast cancer recurrence using machine learning techniques: a systematic review" ACM Comput. Surv. (2016) 10.1145/2988544

[2]

Alapati "Combining clustering with classification: a technique to improve classification accuracy" Lung Cancer (2016)

[3]

Editorial

Nitesh V. Chawla, Nathalie Japkowicz, Aleksander Kotcz

ACM SIGKDD Explorations Newsletter 2004 10.1145/1007730.1007733

[4]

Chen "Xgboost: a scalable tree boosting system" (2016)

[5]

El Houby "Framework of computer aided diagnosis systems for cancer classification based on medical images" J. Med. Syst. (2018) 10.1007/s10916-018-1010-x

[6]

Sociodemographic and economic factors are associated with weight gain between before and after cancer diagnosis: results from the prospective population-based NutriNet-Santé cohort

Philippine Fassier, Laurent Zelek, Patrick Bachmann et al.

Oncotarget 2017 10.18632/oncotarget.17676

[7]

SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary

Alberto Fernández, Salvador García, Francisco Herrera et al.

Journal of Artificial Intelligence Research 2018 10.1613/jair.1.11192

[8]

Huang "Feature selection and cancer classification via sparse logistic regression with the hybrid L1/2+2 regularization" PLoS One (2016) 10.1371/journal.pone.0149675

[9]

Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values

Zhexue Huang

Data Mining and Knowledge Discovery 1998 10.1023/a:1009769707641

[10]

Ikram "Improving accuracy of intrusion detection model using PCA and optimized SVM" J. Comput. Sci. Tech. (2016) 10.20532/cit.2016.1002701

[11]

Kamińska "Breast cancer risk factors" Prz Menopauzalny (2015) 10.5114/pm.2015.54346

[12]

Khormuji "A novel sparse coding algorithm for classification of tumors based on gene expression data" Med. Biol. Eng. Comput. (2016) 10.1007/s11517-015-1382-8

[13]

Kolak "Primary and secondary prevention of breast cancer" Ann. Agric. Environ. Med. (2017) 10.26444/aaem/75943

[14]

Machine learning applications in cancer prognosis and prediction

Konstantina Kourou, Themis P. Exarchos, Konstantinos P. Exarchos et al.

Computational and Structural Biotechnology Journal 2015 10.1016/j.csbj.2014.11.005

[15]

Liu "MALDI-TOF MS combined with magnetic beads for detecting serum protein biomarkers and establishment of boosting decision tree model for diagnosis of colorectal cancer" Int. J. Med. Sci. (2011) 10.7150/ijms.8.39

[16]

Markus "Long-term health risk after breast-cancer radiotherapy: overview of PASSOS methodology and software" Radiat. Prot. Dosimetry (2019) 10.1093/rpd/ncy219

[17]

Mellemkjaer "Risk of second cancer among women with breast cancer" Int. J. Cancer. (2006) 10.1002/ijc.21651

[18]

Nasution "PCA based feature reduction to improve the accuracy of decision tree c4. 5 classification" J. Phys. Conf. (2018) 10.1088/1742-6596/978/1/012058

[19]

Schmidhuber "The Global Nutrient Database: availability of macronutrients and micronutrients in 195 countries from 1980 to 2013" Lancet Planet. Health (2018) 10.1016/s2542-5196(18)30170-0

[20]

optCluster: An R Package for Determining the Optimal Clustering Algorithm

Michael Sekula, Somnath Datta, Susmita Datta

Bioinformation 2017 10.6026/97320630013101

[21]

Shimoda "Using machine-learning approaches to predict non-participation in a nationwide general health check-up scheme" Comput. Methods Programs Biomed. (2018) 10.1016/j.cmpb.2018.05.032

[22]

Sun "A machine learning approach to the accurate prediction of monitor units for a compact proton machine" Med. Phys. (2018) 10.1002/mp.12842

[23]

The 5-year survival rate after breast cancer2019

[24]

The age-adjusted incidence rates: 1979-20162019

[25]

Trivedi "The utility of clustering in prediction tasks" arXiv preprint arXiv (2015)

[26]

Tseng "Application of machine learning to predict the recurrence-proneness for cervical cancer" Neural Comput. Appl. (2014) 10.1007/s00521-013-1359-1

[27]

Tseng "Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence" Artif. Intell. Med. (2017) 10.1016/j.artmed.2017.06.003

[28]

Vural "Classification of breast cancer patients using somatic mutation profiles and machine learning approaches" BMC Syst. Biol. (2016) 10.1186/s12918-016-0306-z

[29]

Wang "Multiclass imbalance problems: analysis and potential solutions" IEEE Trans. Syst. Man Cybern. B Cybern. (2012) 10.1109/tsmcb.2012.2187280

[30]

Wang "Using class imbalance learning for software defect prediction" IEEE T. Reliab. (2013) 10.1109/tr.2013.2259203

[31]

Warren "Multiple malignant tumors. A survey of the literature and statistical study" Am. J. Cancer. (1932)

[32]

Xie "Comparison among dimensionality reduction techniques based on random projection for cancer classification" Comput. Biol. Chem. (2016) 10.1016/j.compbiolchem.2016.09.010

[33]

Ye "A hybrid machine learning scheme to analyze the risk factors of breast cancer outcome in patients with diabetes mellitus" J. Univers. Comput. Sci. (2018) 10.3217/jucs-024-06-0665

[34]

Yousefi "Organ-specific metastasis of breast cancer: molecular and cellular mechanisms underlying lung metastasis" Cell. Oncol. (Dordr). (2018) 10.1007/s13402-018-0376-6

[35]

Yu "Analysis the effect of PCA for feature reduction in non-stationary EEG based motor imagery of BCI system" Optik (Stuttg) (2014) 10.1016/j.ijleo.2013.09.013

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Published: Sep 18, 2019
Vol/Issue: 10
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Chi-Chang Chang, Ssu-Han Chen (2019). Developing a Novel Machine Learning-Based Classification Scheme for Predicting SPCs in Breast Cancer Survivors. Frontiers in Genetics, 10. https://doi.org/10.3389/fgene.2019.00848

Developing a Novel Machine Learning-Based Classification Scheme for Predicting SPCs in Breast Cancer Survivors

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