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A Deep Belief Network and Dempster-Shafer-Based Multiclassifier for the Pathology Stage of Prostate Cancer
Joint Authors
Lee, Ji Youl
Byun, Seok-Soo
Jeong, Chang Wook
Koo, Kyo Chul
Kim, Choung-Soo
Seo, Seong Il
Kim, Jae Kwon
Choi, Mun Joo
Lee, Jong Sik
Hong, Jun Hyuk
Chung, Byung Ha
Park, Yong Hyun
Choi, In Young
Source
Journal of Healthcare Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-03-19
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Object.
Pathologic prediction of prostate cancer can be made by predicting the patient’s prostate metastasis prior to surgery based on biopsy information.
Because biopsy variables associated with pathology have uncertainty regarding individual patient differences, a method for classification according to these variables is needed.
Method.
We propose a deep belief network and Dempster-Shafer- (DBN-DS-) based multiclassifier for the pathologic prediction of prostate cancer.
The DBN-DS learns prostate-specific antigen (PSA), Gleason score, and clinical T stage variable information using three DBNs.
Uncertainty regarding the predicted output was removed from the DBN and combined with information from DS to make a correct decision.
Result.
The new method was validated on pathology data from 6342 patients with prostate cancer.
The pathology stages consisted of organ-confined disease (OCD; 3892 patients) and non-organ-confined disease (NOCD; 2453 patients).
The results showed that the accuracy of the proposed DBN-DS was 81.27%, which is higher than the 64.14% of the Partin table.
Conclusion.
The proposed DBN-DS is more effective than other methods in predicting pathology stage.
The performance is high because of the linear combination using the results of pathology-related features.
The proposed method may be effective in decision support for prostate cancer treatment.
American Psychological Association (APA)
Kim, Jae Kwon& Choi, Mun Joo& Lee, Jong Sik& Hong, Jun Hyuk& Kim, Choung-Soo& Seo, Seong Il…[et al.]. 2018. A Deep Belief Network and Dempster-Shafer-Based Multiclassifier for the Pathology Stage of Prostate Cancer. Journal of Healthcare Engineering،Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1187289
Modern Language Association (MLA)
Kim, Jae Kwon…[et al.]. A Deep Belief Network and Dempster-Shafer-Based Multiclassifier for the Pathology Stage of Prostate Cancer. Journal of Healthcare Engineering No. 2018 (2018), pp.1-8.
https://search.emarefa.net/detail/BIM-1187289
American Medical Association (AMA)
Kim, Jae Kwon& Choi, Mun Joo& Lee, Jong Sik& Hong, Jun Hyuk& Kim, Choung-Soo& Seo, Seong Il…[et al.]. A Deep Belief Network and Dempster-Shafer-Based Multiclassifier for the Pathology Stage of Prostate Cancer. Journal of Healthcare Engineering. 2018. Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1187289
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1187289