Identifying high-risk breast cancer patients using microarray and clinical data

Azni Nasuha Ngisa; Ong Huey Fang

doi:10.1109/BIBM49941.2020.9313175

Identifying high-risk breast cancer patients using microarray and clinical data

Azni Nasuha Ngisa, Ong Huey Fang

Malaysia Sch of Information Technology

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

Abstract

The performance of DNA microarray in breast cancer prediction demonstrates the potential of genome-wide analysis using gene expression data. Therefore, this study proposed a prediction method called GridPCA to identify highrisk breast cancer patients using both microarray and clinical data. The GridSearch and Principal Component Analysis are employed in the proposed method to deal with the high dimensionality of microarray data. The experimental results showed that GridPCA achieved approximately 82% of average predictive accuracy with Decision Tree, K-Nearest Neighbour, Logistic Regression and Support Vector Machine classifiers. In future, the proposed method could be used in developing systems that help doctors in planning, decision making and tailoring appropriate treatments for increasing the survival rate of breast cancer patients.

Original language	English
Title of host publication	2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2020)
Editors	Taesung Park, Young-Rae Cho, Xiaohua Hu, Illhoi Yoo, Hyun Goo Woo, Jianxin Wang, Julio Facelli, Seungyoon Nam, Mingon Kang
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	2040-2044
Number of pages	5
ISBN (Electronic)	9781728162157
ISBN (Print)	9781728162164
DOIs	https://doi.org/10.1109/BIBM49941.2020.9313175
Publication status	Published - 2020
Event	IEEE International Conference on Bioinformatics and Biomedicine 2020 - Virtual, Seoul, Korea, South Duration: 16 Dec 2020 → 19 Dec 2020 https://ieeexplore.ieee.org/xpl/conhome/9312958/proceeding (Proceedings)

Conference

Conference	IEEE International Conference on Bioinformatics and Biomedicine 2020
Abbreviated title	BIBM 2020
Country/Territory	Korea, South
City	Virtual, Seoul
Period	16/12/20 → 19/12/20
Internet address	https://ieeexplore.ieee.org/xpl/conhome/9312958/proceeding (Proceedings)

Keywords

breast cancer
clinical data
microarray
prediction

UN SDGs

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

Access to Document

10.1109/BIBM49941.2020.9313175

Cite this

Ngisa, A. N., & Fang, O. H. (2020). Identifying high-risk breast cancer patients using microarray and clinical data. In T. Park, Y-R. Cho, X. Hu, I. Yoo, H. G. Woo, J. Wang, J. Facelli, S. Nam, & M. Kang (Eds.), 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2020) (pp. 2040-2044). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/BIBM49941.2020.9313175

Ngisa, Azni Nasuha ; Fang, Ong Huey. / Identifying high-risk breast cancer patients using microarray and clinical data. 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2020). editor / Taesung Park ; Young-Rae Cho ; Xiaohua Hu ; Illhoi Yoo ; Hyun Goo Woo ; Jianxin Wang ; Julio Facelli ; Seungyoon Nam ; Mingon Kang. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2020. pp. 2040-2044

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title = "Identifying high-risk breast cancer patients using microarray and clinical data",

abstract = "The performance of DNA microarray in breast cancer prediction demonstrates the potential of genome-wide analysis using gene expression data. Therefore, this study proposed a prediction method called GridPCA to identify highrisk breast cancer patients using both microarray and clinical data. The GridSearch and Principal Component Analysis are employed in the proposed method to deal with the high dimensionality of microarray data. The experimental results showed that GridPCA achieved approximately 82% of average predictive accuracy with Decision Tree, K-Nearest Neighbour, Logistic Regression and Support Vector Machine classifiers. In future, the proposed method could be used in developing systems that help doctors in planning, decision making and tailoring appropriate treatments for increasing the survival rate of breast cancer patients.",

keywords = "breast cancer, clinical data, microarray, prediction",

author = "Ngisa, {Azni Nasuha} and Fang, {Ong Huey}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; IEEE International Conference on Bioinformatics and Biomedicine 2020, BIBM 2020 ; Conference date: 16-12-2020 Through 19-12-2020",

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doi = "10.1109/BIBM49941.2020.9313175",

language = "English",

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booktitle = "2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2020)",

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Ngisa, AN & Fang, OH 2020, Identifying high-risk breast cancer patients using microarray and clinical data. in T Park, Y-R Cho, X Hu, I Yoo, HG Woo, J Wang, J Facelli, S Nam & M Kang (eds), 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2020). IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 2040-2044, IEEE International Conference on Bioinformatics and Biomedicine 2020, Virtual, Seoul, Korea, South, 16/12/20. https://doi.org/10.1109/BIBM49941.2020.9313175

Identifying high-risk breast cancer patients using microarray and clinical data. / Ngisa, Azni Nasuha; Fang, Ong Huey.

2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2020). ed. / Taesung Park; Young-Rae Cho; Xiaohua Hu; Illhoi Yoo; Hyun Goo Woo; Jianxin Wang; Julio Facelli; Seungyoon Nam; Mingon Kang. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2020. p. 2040-2044.

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

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T1 - Identifying high-risk breast cancer patients using microarray and clinical data

AU - Ngisa, Azni Nasuha

AU - Fang, Ong Huey

PY - 2020

Y1 - 2020

N2 - The performance of DNA microarray in breast cancer prediction demonstrates the potential of genome-wide analysis using gene expression data. Therefore, this study proposed a prediction method called GridPCA to identify highrisk breast cancer patients using both microarray and clinical data. The GridSearch and Principal Component Analysis are employed in the proposed method to deal with the high dimensionality of microarray data. The experimental results showed that GridPCA achieved approximately 82% of average predictive accuracy with Decision Tree, K-Nearest Neighbour, Logistic Regression and Support Vector Machine classifiers. In future, the proposed method could be used in developing systems that help doctors in planning, decision making and tailoring appropriate treatments for increasing the survival rate of breast cancer patients.

AB - The performance of DNA microarray in breast cancer prediction demonstrates the potential of genome-wide analysis using gene expression data. Therefore, this study proposed a prediction method called GridPCA to identify highrisk breast cancer patients using both microarray and clinical data. The GridSearch and Principal Component Analysis are employed in the proposed method to deal with the high dimensionality of microarray data. The experimental results showed that GridPCA achieved approximately 82% of average predictive accuracy with Decision Tree, K-Nearest Neighbour, Logistic Regression and Support Vector Machine classifiers. In future, the proposed method could be used in developing systems that help doctors in planning, decision making and tailoring appropriate treatments for increasing the survival rate of breast cancer patients.

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KW - clinical data

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A2 - Wang, Jianxin

A2 - Facelli, Julio

A2 - Nam, Seungyoon

A2 - Kang, Mingon

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ USA

T2 - IEEE International Conference on Bioinformatics and Biomedicine 2020

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ER -

Ngisa AN, Fang OH. Identifying high-risk breast cancer patients using microarray and clinical data. In Park T, Cho Y-R, Hu X, Yoo I, Woo HG, Wang J, Facelli J, Nam S, Kang M, editors, 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2020). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2020. p. 2040-2044 https://doi.org/10.1109/BIBM49941.2020.9313175

Identifying high-risk breast cancer patients using microarray and clinical data

Abstract

Conference

Keywords

UN SDGs

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