Prostate Cancer Classification Based on Best First Search and Taguchi Feature Selection Method

Rahman, MA; Singh, P; Muniyandi, RC; Mery, D; Prasad, M

Prostate Cancer Classification Based on Best First Search and Taguchi Feature Selection Method

Rahman, MA Singh, P Muniyandi, RC Mery, D Prasad, M

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Image and Video Technology, 2020, 11854 LNCS, pp. 325-336
Issue Date:: 2020

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Field	Value	Language
dc.contributor.author	Rahman, MA
dc.contributor.author	Singh, P
dc.contributor.author	Muniyandi, RC
dc.contributor.author	Mery, D
dc.contributor.author	Prasad, M https://orcid.org/0000-0001-9719-2253
dc.date	2019-11-18
dc.date.accessioned	2021-02-17T01:58:08Z
dc.date.available	2021-02-17T01:58:08Z
dc.date.issued	2020
dc.identifier.citation	Image and Video Technology, 2020, 11854 LNCS, pp. 325-336
dc.identifier.isbn	9783030348786
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/146167
dc.description.abstract	© 2019, Springer Nature Switzerland AG. Prostate cancer is the second most common cancer occurring in men worldwide, about 1 in 41 men will die because of prostate cancer. Death rates of prostate cancer increases with age. Even though, it being a serious condition only about 1 man in 9 will be diagnosed with prostate cancer during his lifetime. Accurate and early diagnosis can help clinician to treat the cancer better and save lives. This paper proposes two phases feature selection method to enhance prostate cancer early diagnosis based on artificial neural network. In the first phase, Best First Search method is used to extract the relevant features from original dataset. In the second phase, Taguchi method is used to select the most important feature from the already extracted features from Best First Search method. A public available prostate cancer benchmark dataset is used for experiment, which contains two classes of data normal and abnormal. The proposed method outperforms other existing methods on prostate cancer benchmark dataset with classification accuracy of 98.6%. The proposed approach can help clinicians to reach at more accurate and early diagnosis of different stages of prostate cancer and so that they make most suitable treatment decision to save lives of patients and prevent death due to prostate cancer.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Image and Video Technology
dc.relation.ispartof	Pacific-Rim Symposium on Image and Video Technology
dc.relation.isbasedon	10.1007/978-3-030-34879-3_25
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Prostate Cancer Classification Based on Best First Search and Taguchi Feature Selection Method
dc.type	Conference Proceeding
utslib.citation.volume	11854 LNCS
utslib.location.activity	Sydney, NSW, Australia
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building/School of Architecture
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-02-17T01:57:28Z
pubs.finish-date	2019-11-22
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.start-date	2019-11-18
pubs.volume	11854 LNCS
dc.location	Switzerland

Abstract:

© 2019, Springer Nature Switzerland AG. Prostate cancer is the second most common cancer occurring in men worldwide, about 1 in 41 men will die because of prostate cancer. Death rates of prostate cancer increases with age. Even though, it being a serious condition only about 1 man in 9 will be diagnosed with prostate cancer during his lifetime. Accurate and early diagnosis can help clinician to treat the cancer better and save lives. This paper proposes two phases feature selection method to enhance prostate cancer early diagnosis based on artificial neural network. In the first phase, Best First Search method is used to extract the relevant features from original dataset. In the second phase, Taguchi method is used to select the most important feature from the already extracted features from Best First Search method. A public available prostate cancer benchmark dataset is used for experiment, which contains two classes of data normal and abnormal. The proposed method outperforms other existing methods on prostate cancer benchmark dataset with classification accuracy of 98.6%. The proposed approach can help clinicians to reach at more accurate and early diagnosis of different stages of prostate cancer and so that they make most suitable treatment decision to save lives of patients and prevent death due to prostate cancer.

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http://hdl.handle.net/10453/146167