A comparison of hybrid neural network based breast cancer diagnosis systems

Hsine Jen Tsai; Hao Chun Lu; Tung Huan Wu; Chiang Sheng Lee

doi:10.1007/978-3-319-20895-4_59

A comparison of hybrid neural network based breast cancer diagnosis systems

Hsine Jen Tsai^*, Hao Chun Lu, Tung Huan Wu, Chiang Sheng Lee

^*Corresponding author for this work

Department of Management Science

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

Abstract

Breast cancer is the second leading cause of death among the women aged between 40 and 59 in the world. The diagnosis of such disease has been a challenging research problem. With the advancement of artificial intelligence in medical science, numerous AI based breast cancer diagnosis system have been proposed. Many researches combine different algorithms to develop hybrid systems to improve the diagnosis accuracy. In this study, we propose three artificial neural network based hybrid diagnosis systems respectively combining association rule, correlation and genetic algorithm. The effectiveness of these systems is examined on Wisconsin Breast Cancer Dataset. We then compare the accuracy of these three hybrid diagnosis systems. The results indicated that the neural network combining with association rule not only has excellent dimensionality reduction ability but also has the similar accurate prediction with correlation based neural network which has best accurate prediction rate among all three systems compared.

Original language	English
Title of host publication	HCI in Business - 2nd International Conference, HCIB 2015 Held as Part of HCI International 2015, Proceedings
Editors	Fiona Fui-Hoon Nah, Chuan-Hoo Tan
Publisher	Springer Verlag
Pages	633-639
Number of pages	7
ISBN (Print)	9783319208947
DOIs	https://doi.org/10.1007/978-3-319-20895-4_59
State	Published - 2015
Event	2nd International Conference on HCI in Business, HCIB 2015 Held as Part of 17th International Conference on Human-Computer Interaction, HCI International 2015 - Los Angeles, United States Duration: 2 Aug 2015 → 7 Aug 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9191
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Conference on HCI in Business, HCIB 2015 Held as Part of 17th International Conference on Human-Computer Interaction, HCI International 2015
Country/Territory	United States
City	Los Angeles
Period	2/08/15 → 7/08/15

Keywords

Association rule
Genetic algorithm
Medical artificial intelligence
Neural network

UN SDGs

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

Access to Document

10.1007/978-3-319-20895-4_59

Cite this

Tsai, H. J., Lu, H. C., Wu, T. H., & Lee, C. S. (2015). A comparison of hybrid neural network based breast cancer diagnosis systems. In F. F.-H. Nah, & C.-H. Tan (Eds.), HCI in Business - 2nd International Conference, HCIB 2015 Held as Part of HCI International 2015, Proceedings (pp. 633-639). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9191). Springer Verlag. https://doi.org/10.1007/978-3-319-20895-4_59

Tsai, Hsine Jen ; Lu, Hao Chun ; Wu, Tung Huan et al. / A comparison of hybrid neural network based breast cancer diagnosis systems. HCI in Business - 2nd International Conference, HCIB 2015 Held as Part of HCI International 2015, Proceedings. editor / Fiona Fui-Hoon Nah ; Chuan-Hoo Tan. Springer Verlag, 2015. pp. 633-639 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Breast cancer is the second leading cause of death among the women aged between 40 and 59 in the world. The diagnosis of such disease has been a challenging research problem. With the advancement of artificial intelligence in medical science, numerous AI based breast cancer diagnosis system have been proposed. Many researches combine different algorithms to develop hybrid systems to improve the diagnosis accuracy. In this study, we propose three artificial neural network based hybrid diagnosis systems respectively combining association rule, correlation and genetic algorithm. The effectiveness of these systems is examined on Wisconsin Breast Cancer Dataset. We then compare the accuracy of these three hybrid diagnosis systems. The results indicated that the neural network combining with association rule not only has excellent dimensionality reduction ability but also has the similar accurate prediction with correlation based neural network which has best accurate prediction rate among all three systems compared.",

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Tsai, HJ, Lu, HC, Wu, TH & Lee, CS 2015, A comparison of hybrid neural network based breast cancer diagnosis systems. in FF-H Nah & C-H Tan (eds), HCI in Business - 2nd International Conference, HCIB 2015 Held as Part of HCI International 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9191, Springer Verlag, pp. 633-639, 2nd International Conference on HCI in Business, HCIB 2015 Held as Part of 17th International Conference on Human-Computer Interaction, HCI International 2015, Los Angeles, United States, 2/08/15. https://doi.org/10.1007/978-3-319-20895-4_59

A comparison of hybrid neural network based breast cancer diagnosis systems. / Tsai, Hsine Jen; Lu, Hao Chun; Wu, Tung Huan et al.
HCI in Business - 2nd International Conference, HCIB 2015 Held as Part of HCI International 2015, Proceedings. ed. / Fiona Fui-Hoon Nah; Chuan-Hoo Tan. Springer Verlag, 2015. p. 633-639 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9191).

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

TY - GEN

T1 - A comparison of hybrid neural network based breast cancer diagnosis systems

AU - Tsai, Hsine Jen

AU - Lu, Hao Chun

AU - Wu, Tung Huan

AU - Lee, Chiang Sheng

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - Breast cancer is the second leading cause of death among the women aged between 40 and 59 in the world. The diagnosis of such disease has been a challenging research problem. With the advancement of artificial intelligence in medical science, numerous AI based breast cancer diagnosis system have been proposed. Many researches combine different algorithms to develop hybrid systems to improve the diagnosis accuracy. In this study, we propose three artificial neural network based hybrid diagnosis systems respectively combining association rule, correlation and genetic algorithm. The effectiveness of these systems is examined on Wisconsin Breast Cancer Dataset. We then compare the accuracy of these three hybrid diagnosis systems. The results indicated that the neural network combining with association rule not only has excellent dimensionality reduction ability but also has the similar accurate prediction with correlation based neural network which has best accurate prediction rate among all three systems compared.

AB - Breast cancer is the second leading cause of death among the women aged between 40 and 59 in the world. The diagnosis of such disease has been a challenging research problem. With the advancement of artificial intelligence in medical science, numerous AI based breast cancer diagnosis system have been proposed. Many researches combine different algorithms to develop hybrid systems to improve the diagnosis accuracy. In this study, we propose three artificial neural network based hybrid diagnosis systems respectively combining association rule, correlation and genetic algorithm. The effectiveness of these systems is examined on Wisconsin Breast Cancer Dataset. We then compare the accuracy of these three hybrid diagnosis systems. The results indicated that the neural network combining with association rule not only has excellent dimensionality reduction ability but also has the similar accurate prediction with correlation based neural network which has best accurate prediction rate among all three systems compared.

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SN - 9783319208947

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - HCI in Business - 2nd International Conference, HCIB 2015 Held as Part of HCI International 2015, Proceedings

A2 - Nah, Fiona Fui-Hoon

A2 - Tan, Chuan-Hoo

PB - Springer Verlag

T2 - 2nd International Conference on HCI in Business, HCIB 2015 Held as Part of 17th International Conference on Human-Computer Interaction, HCI International 2015

Y2 - 2 August 2015 through 7 August 2015

ER -

Tsai HJ, Lu HC, Wu TH, Lee CS. A comparison of hybrid neural network based breast cancer diagnosis systems. In Nah FFH, Tan CH, editors, HCI in Business - 2nd International Conference, HCIB 2015 Held as Part of HCI International 2015, Proceedings. Springer Verlag. 2015. p. 633-639. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-20895-4_59

A comparison of hybrid neural network based breast cancer diagnosis systems

Abstract

Publication series

Conference

Keywords

UN SDGs

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