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

^*此作品的通信作者

管理科學系

研究成果: Conference contribution › 同行評審

摘要

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.

原文	English
主出版物標題	HCI in Business - 2nd International Conference, HCIB 2015 Held as Part of HCI International 2015, Proceedings
編輯	Fiona Fui-Hoon Nah, Chuan-Hoo Tan
發行者	Springer Verlag
頁面	633-639
頁數	7
ISBN（列印）	9783319208947
DOIs	https://doi.org/10.1007/978-3-319-20895-4_59
出版狀態	Published - 2015
事件	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, 美國持續時間: 2 8月 2015 → 7 8月 2015

出版系列

名字	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
卷	9191
ISSN（列印）	0302-9743
ISSN（電子）	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
國家/地區	美國
城市	Los Angeles
期間	2/08/15 → 7/08/15

UN SDG

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

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

Tsai, Hsine Jen ; Lu, Hao Chun ; Wu, Tung Huan 等. / 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. 編輯 / Fiona Fui-Hoon Nah ; Chuan-Hoo Tan. Springer Verlag, 2015. 頁 633-639 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A comparison of hybrid neural network based breast cancer diagnosis systems",

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.",

keywords = "Association rule, Genetic algorithm, Medical artificial intelligence, Neural network",

author = "Tsai, {Hsine Jen} and Lu, {Hao Chun} and Wu, {Tung Huan} and Lee, {Chiang Sheng}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 2nd International Conference on HCI in Business, HCIB 2015 Held as Part of 17th International Conference on Human-Computer Interaction, HCI International 2015 ; Conference date: 02-08-2015 Through 07-08-2015",

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Tsai, HJ, Lu, HC, Wu, TH & Lee, CS 2015, A comparison of hybrid neural network based breast cancer diagnosis systems. 於 FF-H Nah & C-H Tan (編輯), 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), 卷 9191, Springer Verlag, 頁 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, 美國, 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 等.
HCI in Business - 2nd International Conference, HCIB 2015 Held as Part of HCI International 2015, Proceedings. 編輯 / 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); 卷 9191).

研究成果: Conference contribution › 同行評審

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.

KW - Association rule

KW - Genetic algorithm

KW - Medical artificial intelligence

KW - Neural network

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U2 - 10.1007/978-3-319-20895-4_59

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

M3 - Conference contribution

AN - SCOPUS:84947255337

SN - 9783319208947

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

SP - 633

EP - 639

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. 於 Nah FFH, Tan CH, 編輯, 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

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