%0 Journal Article
%T A Clinical Decision Support System for Femoral Peripheral Arterial Disease Treatment
%A Alk£¿n Yurtkuran
%A Mustafa Tok
%A Erdal Emel
%J Computational and Mathematical Methods in Medicine
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/898041
%X One of the major challenges of providing reliable healthcare services is to diagnose and treat diseases in an accurate and timely manner. Recently, many researchers have successfully used artificial neural networks as a diagnostic assessment tool. In this study, the validation of such an assessment tool has been developed for treatment of the femoral peripheral arterial disease using a radial basis function neural network (RBFNN). A data set for training the RBFNN has been prepared by analyzing records of patients who had been treated by the thoracic and cardiovascular surgery clinic of a university hospital. The data set includes 186 patient records having 16 characteristic features associated with a binary treatment decision, namely, being a medical or a surgical one. K-means clustering algorithm has been used to determine the parameters of radial basis functions and the number of hidden nodes of the RBFNN is determined experimentally. For performance evaluation, the proposed RBFNN was compared to three different multilayer perceptron models having Pareto optimal hidden layer combinations using various performance indicators. Results of comparison indicate that the RBFNN can be used as an effective assessment tool for femoral peripheral arterial disease treatment. 1. Introduction Various engineering techniques have been adapted to health care delivery systems and the quality of health care services has been improved using these artificial intelligence techniques. It has been proven that introducing machine learning tools into clinical decision support systems can easily increase the decision accuracy and decrease costs and the dependency on highly qualified specialists. Since artificial neural networks (ANN) can easily be trained for identifying the patterns and extracting rules using a small number of cases, they are widely used as a powerful tool for clinical decision support systems [1]. Peripheral arterial disease (PAD) is a common pathologic disease worldwide. Peripheral arterial disease is a disease in which plaque, which is made up of fat, cholesterol, calcium, fibrous tissue, and other substances in the blood, builds up in the arteries that carry blood to head, organs, and limbs. PAD affects more than 30 million people worldwide, and while it can strike anyone, it is most common in people over age 65 [2]. PAD is associated with a significant burden in terms of morbidity and mortality, due to claudication, rest pain, ulcerations, and amputations. In case of mild or moderate peripheral arterial diseases, a medical or conservative therapy can be
%U http://www.hindawi.com/journals/cmmm/2013/898041/