Rule Discovery from Breast Cancer Risk Factors using Association Rule Mining

Md Faisal Kabir; Simone A. Ludwig; Abu Saleh Abdullah

doi:10.1109/BigData.2018.8622028

Rule Discovery from Breast Cancer Risk Factors using Association Rule Mining

Md Faisal Kabir, Simone A. Ludwig, Abu Saleh Abdullah

School of Science, Engineering & Technology (Harrisburg)

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

7 Scopus citations

Abstract

Breast cancer is the most common cancer in women worldwide. Prevention of breast cancer through risk factors reduction is a significant concern to decrease its impact on the population. Attaining or detecting significant information in the form of rules is the key to prevent breast cancer. Our objective is to find hidden but important knowledge of the form of rules from the risk factors data set of breast cancer. Mining rules is one of the vital tasks of data mining as rules provide concise statement of potentially important information that is easily understood by end users. In this paper, we use association rule mining, a data mining technique to attain information in the form of rules from breast cancer risk factors data that could be useful to initiate prevention strategies. We discovered rules of both breast cancer and non-breast cancer patients so that we can understand and compare the characteristics of both breast cancer and non-breast cancer individuals. The experimental results show that generated or mined rules hold the highest confidence level.

Original language	English (US)
Title of host publication	Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
Editors	Naoki Abe, Huan Liu, Calton Pu, Xiaohua Hu, Nesreen Ahmed, Mu Qiao, Yang Song, Donald Kossmann, Bing Liu, Kisung Lee, Jiliang Tang, Jingrui He, Jeffrey Saltz
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2433-2441
Number of pages	9
ISBN (Electronic)	9781538650356
DOIs	https://doi.org/10.1109/BigData.2018.8622028
State	Published - Jul 2 2018
Event	2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States Duration: Dec 10 2018 → Dec 13 2018

Publication series

Name	Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

Conference

Conference	2018 IEEE International Conference on Big Data, Big Data 2018
Country/Territory	United States
City	Seattle
Period	12/10/18 → 12/13/18

All Science Journal Classification (ASJC) codes

Computer Science Applications
Information Systems

UN SDGs

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

Access to Document

10.1109/BigData.2018.8622028

Cite this

Kabir, M. F., Ludwig, S. A., & Abdullah, A. S. (2018). Rule Discovery from Breast Cancer Risk Factors using Association Rule Mining. In N. Abe, H. Liu, C. Pu, X. Hu, N. Ahmed, M. Qiao, Y. Song, D. Kossmann, B. Liu, K. Lee, J. Tang, J. He, & J. Saltz (Eds.), Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 (pp. 2433-2441). Article 8622028 (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2018.8622028

Kabir, Md Faisal ; Ludwig, Simone A. ; Abdullah, Abu Saleh. / Rule Discovery from Breast Cancer Risk Factors using Association Rule Mining. Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. editor / Naoki Abe ; Huan Liu ; Calton Pu ; Xiaohua Hu ; Nesreen Ahmed ; Mu Qiao ; Yang Song ; Donald Kossmann ; Bing Liu ; Kisung Lee ; Jiliang Tang ; Jingrui He ; Jeffrey Saltz. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 2433-2441 (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018).

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title = "Rule Discovery from Breast Cancer Risk Factors using Association Rule Mining",

abstract = "Breast cancer is the most common cancer in women worldwide. Prevention of breast cancer through risk factors reduction is a significant concern to decrease its impact on the population. Attaining or detecting significant information in the form of rules is the key to prevent breast cancer. Our objective is to find hidden but important knowledge of the form of rules from the risk factors data set of breast cancer. Mining rules is one of the vital tasks of data mining as rules provide concise statement of potentially important information that is easily understood by end users. In this paper, we use association rule mining, a data mining technique to attain information in the form of rules from breast cancer risk factors data that could be useful to initiate prevention strategies. We discovered rules of both breast cancer and non-breast cancer patients so that we can understand and compare the characteristics of both breast cancer and non-breast cancer individuals. The experimental results show that generated or mined rules hold the highest confidence level.",

author = "Kabir, {Md Faisal} and Ludwig, {Simone A.} and Abdullah, {Abu Saleh}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Big Data, Big Data 2018 ; Conference date: 10-12-2018 Through 13-12-2018",

year = "2018",

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doi = "10.1109/BigData.2018.8622028",

language = "English (US)",

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editor = "Naoki Abe and Huan Liu and Calton Pu and Xiaohua Hu and Nesreen Ahmed and Mu Qiao and Yang Song and Donald Kossmann and Bing Liu and Kisung Lee and Jiliang Tang and Jingrui He and Jeffrey Saltz",

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Kabir, MF, Ludwig, SA & Abdullah, AS 2018, Rule Discovery from Breast Cancer Risk Factors using Association Rule Mining. in N Abe, H Liu, C Pu, X Hu, N Ahmed, M Qiao, Y Song, D Kossmann, B Liu, K Lee, J Tang, J He & J Saltz (eds), Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018., 8622028, Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, Institute of Electrical and Electronics Engineers Inc., pp. 2433-2441, 2018 IEEE International Conference on Big Data, Big Data 2018, Seattle, United States, 12/10/18. https://doi.org/10.1109/BigData.2018.8622028

Rule Discovery from Breast Cancer Risk Factors using Association Rule Mining. / Kabir, Md Faisal; Ludwig, Simone A.; Abdullah, Abu Saleh.
Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. ed. / Naoki Abe; Huan Liu; Calton Pu; Xiaohua Hu; Nesreen Ahmed; Mu Qiao; Yang Song; Donald Kossmann; Bing Liu; Kisung Lee; Jiliang Tang; Jingrui He; Jeffrey Saltz. Institute of Electrical and Electronics Engineers Inc., 2018. p. 2433-2441 8622028 (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018).

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

TY - GEN

T1 - Rule Discovery from Breast Cancer Risk Factors using Association Rule Mining

AU - Kabir, Md Faisal

AU - Ludwig, Simone A.

AU - Abdullah, Abu Saleh

PY - 2018/7/2

Y1 - 2018/7/2

N2 - Breast cancer is the most common cancer in women worldwide. Prevention of breast cancer through risk factors reduction is a significant concern to decrease its impact on the population. Attaining or detecting significant information in the form of rules is the key to prevent breast cancer. Our objective is to find hidden but important knowledge of the form of rules from the risk factors data set of breast cancer. Mining rules is one of the vital tasks of data mining as rules provide concise statement of potentially important information that is easily understood by end users. In this paper, we use association rule mining, a data mining technique to attain information in the form of rules from breast cancer risk factors data that could be useful to initiate prevention strategies. We discovered rules of both breast cancer and non-breast cancer patients so that we can understand and compare the characteristics of both breast cancer and non-breast cancer individuals. The experimental results show that generated or mined rules hold the highest confidence level.

AB - Breast cancer is the most common cancer in women worldwide. Prevention of breast cancer through risk factors reduction is a significant concern to decrease its impact on the population. Attaining or detecting significant information in the form of rules is the key to prevent breast cancer. Our objective is to find hidden but important knowledge of the form of rules from the risk factors data set of breast cancer. Mining rules is one of the vital tasks of data mining as rules provide concise statement of potentially important information that is easily understood by end users. In this paper, we use association rule mining, a data mining technique to attain information in the form of rules from breast cancer risk factors data that could be useful to initiate prevention strategies. We discovered rules of both breast cancer and non-breast cancer patients so that we can understand and compare the characteristics of both breast cancer and non-breast cancer individuals. The experimental results show that generated or mined rules hold the highest confidence level.

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M3 - Conference contribution

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T3 - Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

SP - 2433

EP - 2441

BT - Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

A2 - Abe, Naoki

A2 - Liu, Huan

A2 - Pu, Calton

A2 - Hu, Xiaohua

A2 - Ahmed, Nesreen

A2 - Qiao, Mu

A2 - Song, Yang

A2 - Kossmann, Donald

A2 - Liu, Bing

A2 - Lee, Kisung

A2 - Tang, Jiliang

A2 - He, Jingrui

A2 - Saltz, Jeffrey

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE International Conference on Big Data, Big Data 2018

Y2 - 10 December 2018 through 13 December 2018

ER -

Kabir MF, Ludwig SA, Abdullah AS. Rule Discovery from Breast Cancer Risk Factors using Association Rule Mining. In Abe N, Liu H, Pu C, Hu X, Ahmed N, Qiao M, Song Y, Kossmann D, Liu B, Lee K, Tang J, He J, Saltz J, editors, Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 2433-2441. 8622028. (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018). doi: 10.1109/BigData.2018.8622028

Rule Discovery from Breast Cancer Risk Factors using Association Rule Mining

Abstract

Publication series

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All Science Journal Classification (ASJC) codes

UN SDGs

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