Designing a Clinical Decision Support System Based on Artificial Neural Network for Early Detection of Prostate Cancer and Differentiation from Benign Prostatic Hyperplasia *

Document Type : Original Article

Authors
Mustafa Ghaderzadeh 1
Farahnaz Sadoughi 2
Arvin Ketabat 1
1 Health Information Management, School of Health Management and Information Sciences, Tehran University of Medical Sciences, Tehran, Iran
2 Associate Professor, Health Information Management, School of Health Management and Information Sciences, Tehran University of Medical Sciences, Tehran, Iran.
Abstract
Introduction: In recent years, the concepts of artificial neural networks (ANN) have extensively
undergone remarkable development in early detection and classification of diseases such as
benign prostatic hyperplasia (BPH). The usage of ANN has become widely accepted in medical
applications owing to its potential capabilities for detecting the complex interactions among
variables, diagnosis and diseases’ modeling. The present study aimed to design and implement a
decision support system (DSS) based on ANN for early detection of prostate cancer.
Methods: This survey design was conducted through data collection among 360 males with
prostate abnormalities in Urology Department of Imam Khomeini Hospital, Tehran, Iran, from
January 2008 to March 2011. In order to assess the performance and accuracy of the designed
system, sensitivity, specificity and receiver-operating characteristics (ROC) curve were used as
the indicators of distinguishing prostate cancers from BPH. In order to implement DSS in this
study, scaled conjugate gradient (SCG) algorithm was used as the main algorithm for early
detection of prostate cancer from benign prostate.
Results: The proposed intelligent ANN-based system can be used as a strong diagnostic tool with
97.0% specificity and 92.1% sensitivity for detecting the prostate cancer and to differentiate it
from BPH. The results indicated a high potential of artificial neural network as a strong tool in
classification of prostatic neoplasia diseases.
Conclusion: A medical decision support system was used aiming to help medical experts in their
classification and early detection of prostatic neoplasia disorders in the present study. Such
artificial intelligent-based medical intelligent systems, particularly for neural networks, can help
physicians in accurate decision-making concerning prostate cancer and BPH. Using such
systems, specialists would be able to eliminate or minimize unnecessary biopsy and reduce
diagnostic costs. In addition, such systems can accelerate the diagnostic detection time.
Keywords
Decision Support System
Prostatic Neoplasia
Artificial Neural Network
Sensitivity
Specificity
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Health Information Management
Volume 9, Issue 4
September and October 2012
Pages 457-464