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Journal of Healthcare Engineering
Volume 2018, Article ID 4651582, 8 pages
https://doi.org/10.1155/2018/4651582
Research Article

A Deep Belief Network and Dempster-Shafer-Based Multiclassifier for the Pathology Stage of Prostate Cancer

1Department of Computer Science and Information Engineering, Inha University, InhaRo 100, Nam-gu, Incheon, Republic of Korea
2Department of Medical Informatics, College of Medicine, The Catholic University of Seoul, 222 Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
3Department of Urology, University of Ulsan College of Medicine, Seoul, Republic of Korea
4Department of Urology, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
5Department of Urology, Seoul National University College of Medicine, Seoul, Republic of Korea
6Department of Urology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
7Department of Urology, Yonsei University College of Medicine, Seoul, Republic of Korea
8Department of Urology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea

Correspondence should be addressed to In Young Choi; rk.ca.cilohtac@iohcyi

Received 25 August 2017; Accepted 9 January 2018; Published 19 March 2018

Academic Editor: Ming-Yuan Hsieh

Copyright © 2018 Jae Kwon Kim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.