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Pre-operative prediction of advanced prostatic cancer using clinical decision support systems: accuracy comparison between support vector machine and artificial neural network

Title
Pre-operative prediction of advanced prostatic cancer using clinical decision support systems: accuracy comparison between support vector machine and artificial neural network
Authors
Sang Youn Kim;Sung Kyoung Moon;Hak Jong Lee;Jiwon Lee;Seung Hyup Kim;Jeong Yeon Cho;Chang Kyu Sung;Sung Il Hwang;Dae Chul Jung
Issue Date
2011
Journal Title
Korean Journal of Radiology
ISSN
1229-6929
Citation
Korean Journal of Radiology, Vol.12(5) : 588~594, 2011
Abstract
OBJECTIVE: The purpose of the current study was to develop support vector machine (SVM) and artificial neural network (ANN) models for the pre-operative prediction of advanced prostate cancer by using the parameters acquired from transrectal ultrasound (TRUS)-guided prostate biopsies, and to compare the accuracies between the two models. MATERIALS AND METHODS: Five hundred thirty-two consecutive patients who underwent prostate biopsies and prostatectomies for prostate cancer were divided into the training and test groups (n = 300 versus n = 232). From the data in the training group, two clinical decision support systems (CDSSs-[SVM and ANN]) were constructed with input (age, prostate specific antigen level, digital rectal examination, and five biopsy parameters) and output data (the probability for advanced prostate cancer [> pT3a]). From the data of the test group, the accuracy of output data was evaluated. The areas under the receiver operating characteristic (ROC) curve (AUC) were calculated to summarize the overall performances, and a comparison of the ROC curves was performed (p < 0.05). RESULTS: The AUC of SVM and ANN is 0.805 and 0.719, respectively (p = 0.020), in the pre-operative prediction of advanced prostate cancer. CONCLUSION: The performance of SVM is superior to ANN in the pre-operative prediction of advanced prostate cancer.
URI

http://ir.ymlib.yonsei.ac.kr/handle/22282913/94519
DOI
10.3348/kjr.2011.12.5.588
Appears in Collections:
1. 연구논문 > 1. College of Medicine > Dept. of Radiology
Yonsei Authors
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