Text Mining Algorithms for Cancer Diagnostics

Natasia Fernandez; Vaibhav Anu

doi:10.1109/BigData52589.2021.9671668

Text Mining Algorithms for Cancer Diagnostics

Natasia Fernandez
, Vaibhav Anu

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In recent years, several text mining models have been developed for supporting the process of early and accurate cancer diagnosis. This study provides a comparative analysis of existing text mining algorithms for cancer diagnostics. This study will support researchers and practitioners when choosing the most accurate algorithm when diagnosing specific types of cancer.

Original language	English
Title of host publication	Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
Editors	Yixin Chen, Heiko Ludwig, Yicheng Tu, Usama Fayyad, Xingquan Zhu, Xiaohua Tony Hu, Suren Byna, Xiong Liu, Jianping Zhang, Shirui Pan, Vagelis Papalexakis, Jianwu Wang, Alfredo Cuzzocrea, Carlos Ordonez
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5891-5893
Number of pages	3
ISBN (Electronic)	9781665439022
DOIs	https://doi.org/10.1109/BigData52589.2021.9671668
State	Published - 2021
Event	2021 IEEE International Conference on Big Data, Big Data 2021 - Virtual, Online, United States Duration: 15 Dec 2021 → 18 Dec 2021

Publication series

Name	Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021

Conference

Conference	2021 IEEE International Conference on Big Data, Big Data 2021
Country/Territory	United States
City	Virtual, Online
Period	15/12/21 → 18/12/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

10.1109/BigData52589.2021.9671668

Cite this

Fernandez, N., & Anu, V. (2021). Text Mining Algorithms for Cancer Diagnostics. In Y. Chen, H. Ludwig, Y. Tu, U. Fayyad, X. Zhu, X. T. Hu, S. Byna, X. Liu, J. Zhang, S. Pan, V. Papalexakis, J. Wang, A. Cuzzocrea, & C. Ordonez (Eds.), Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021 (pp. 5891-5893). (Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData52589.2021.9671668

Fernandez, Natasia ; Anu, Vaibhav. / Text Mining Algorithms for Cancer Diagnostics. Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. editor / Yixin Chen ; Heiko Ludwig ; Yicheng Tu ; Usama Fayyad ; Xingquan Zhu ; Xiaohua Tony Hu ; Suren Byna ; Xiong Liu ; Jianping Zhang ; Shirui Pan ; Vagelis Papalexakis ; Jianwu Wang ; Alfredo Cuzzocrea ; Carlos Ordonez. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 5891-5893 (Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021).

@inproceedings{66a932ac843843c19c9c52492ac4746a,

title = "Text Mining Algorithms for Cancer Diagnostics",

abstract = "In recent years, several text mining models have been developed for supporting the process of early and accurate cancer diagnosis. This study provides a comparative analysis of existing text mining algorithms for cancer diagnostics. This study will support researchers and practitioners when choosing the most accurate algorithm when diagnosing specific types of cancer.",

author = "Natasia Fernandez and Vaibhav Anu",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Big Data, Big Data 2021 ; Conference date: 15-12-2021 Through 18-12-2021",

year = "2021",

doi = "10.1109/BigData52589.2021.9671668",

language = "English",

series = "Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "5891--5893",

editor = "Yixin Chen and Heiko Ludwig and Yicheng Tu and Usama Fayyad and Xingquan Zhu and Hu, \{Xiaohua Tony\} and Suren Byna and Xiong Liu and Jianping Zhang and Shirui Pan and Vagelis Papalexakis and Jianwu Wang and Alfredo Cuzzocrea and Carlos Ordonez",

booktitle = "Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021",

}

Fernandez, N & Anu, V 2021, Text Mining Algorithms for Cancer Diagnostics. in Y Chen, H Ludwig, Y Tu, U Fayyad, X Zhu, XT Hu, S Byna, X Liu, J Zhang, S Pan, V Papalexakis, J Wang, A Cuzzocrea & C Ordonez (eds), Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021, Institute of Electrical and Electronics Engineers Inc., pp. 5891-5893, 2021 IEEE International Conference on Big Data, Big Data 2021, Virtual, Online, United States, 15/12/21. https://doi.org/10.1109/BigData52589.2021.9671668

Text Mining Algorithms for Cancer Diagnostics. / Fernandez, Natasia; Anu, Vaibhav.
Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. ed. / Yixin Chen; Heiko Ludwig; Yicheng Tu; Usama Fayyad; Xingquan Zhu; Xiaohua Tony Hu; Suren Byna; Xiong Liu; Jianping Zhang; Shirui Pan; Vagelis Papalexakis; Jianwu Wang; Alfredo Cuzzocrea; Carlos Ordonez. Institute of Electrical and Electronics Engineers Inc., 2021. p. 5891-5893 (Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Text Mining Algorithms for Cancer Diagnostics

AU - Fernandez, Natasia

AU - Anu, Vaibhav

PY - 2021

Y1 - 2021

N2 - In recent years, several text mining models have been developed for supporting the process of early and accurate cancer diagnosis. This study provides a comparative analysis of existing text mining algorithms for cancer diagnostics. This study will support researchers and practitioners when choosing the most accurate algorithm when diagnosing specific types of cancer.

AB - In recent years, several text mining models have been developed for supporting the process of early and accurate cancer diagnosis. This study provides a comparative analysis of existing text mining algorithms for cancer diagnostics. This study will support researchers and practitioners when choosing the most accurate algorithm when diagnosing specific types of cancer.

UR - https://www.scopus.com/pages/publications/85125350739

U2 - 10.1109/BigData52589.2021.9671668

DO - 10.1109/BigData52589.2021.9671668

M3 - Conference contribution

AN - SCOPUS:85125350739

T3 - Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021

SP - 5891

EP - 5893

BT - Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021

A2 - Chen, Yixin

A2 - Ludwig, Heiko

A2 - Tu, Yicheng

A2 - Fayyad, Usama

A2 - Zhu, Xingquan

A2 - Hu, Xiaohua Tony

A2 - Byna, Suren

A2 - Liu, Xiong

A2 - Zhang, Jianping

A2 - Pan, Shirui

A2 - Papalexakis, Vagelis

A2 - Wang, Jianwu

A2 - Cuzzocrea, Alfredo

A2 - Ordonez, Carlos

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE International Conference on Big Data, Big Data 2021

Y2 - 15 December 2021 through 18 December 2021

ER -

Fernandez N, Anu V. Text Mining Algorithms for Cancer Diagnostics. In Chen Y, Ludwig H, Tu Y, Fayyad U, Zhu X, Hu XT, Byna S, Liu X, Zhang J, Pan S, Papalexakis V, Wang J, Cuzzocrea A, Ordonez C, editors, Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 5891-5893. (Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021). doi: 10.1109/BigData52589.2021.9671668

Text Mining Algorithms for Cancer Diagnostics

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