Title: Artificial neural networks for medical diagnosis using biomedical dataset

Authors: Qeethara Al-Shayea; Ghaleb El-Refae; Saad Yaseen

Addresses: Department of MIS, Al Zaytoonah University of Jordan, P.O. Box 130 Amman 11733, Jordan ' Department of Finance and Banking, Al Ain University of Science and Technology, P.O. Box 64141 Al Ain, United Arab Emirates ' Department of MIS, Al-Zaytoonah University of Jordan, P.O. Box 130 Amman 11733, Jordan

Abstract: Artificial neural networks are a promising field in medical diagnostic applications. The goal of this study is to propose a neural network for medical diagnosis. A feed-forward back propagation neural network with tan-sigmoid transfer functions is used in this paper. The dataset is obtained from UCI machine learning repository. The results of applying the proposed neural network to distinguish between healthy patients and patients with disease based upon biomedical data in all cases show the ability of the network to learn the patterns corresponding to symptoms of the person. Three cases are studied. In the diagnosis of acute nephritis disease; the percent correctly classified in the simulation sample by the feed-forward back propagation network is 100% while in the diagnosis of heart disease; the percent correctly classified in the simulation sample by the feed-forward back propagation network is approximately 88%. On the other hand, in the diagnosis of disk hernia or spondylolisthesis; the percent correctly classified in the simulation sample is approximately 82%. Receiver operating characteristics (ROCs) curve are used to evaluate diagnosis for decision support.

Keywords: artificial neural networks; ANNs; medical diagnosis; feedforward back propagation networks; receiver operating characteristic; ROC curves; artificial intelligence; decision support systems; DSS; biomedical datasets; healthy patients; diseases; acute nephritis disease; simulation; heart disease; disk hernia; spondylolisthesis.

DOI: 10.1504/IJBHR.2013.054519

International Journal of Behavioural and Healthcare Research, 2013 Vol.4 No.1, pp.45 - 63

Published online: 21 Jun 2014 *

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