title: A clinical decision support system for femoral peripheral arterial disease treatment.
abstract: 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.
authors.affiliation: Department of Industrial Engineering, Faculty of Engineering, Görükle Campus, Uludag University, 16059 Bursa, Turkey.
authors.forename: Alkın
authors.initials: A
authors.lastname: Yurtkuran
authors.affiliation: Department of Thoracic and Cardiovascular Surgery, Faculty of Medicine, Görükle Campus, Uludag University, 16059 Bursa, Turkey.
authors.forename: Mustafa
authors.initials: M
authors.lastname: Tok
authors.affiliation: Department of Industrial Engineering, Faculty of Engineering, Görükle Campus, Uludag University, 16059 Bursa, Turkey.
authors.forename: Erdal
authors.initials: E
authors.lastname: Emel
mesh_terms: D000368:Aged; D000465:Algorithms; D019540:Area Under Curve; D001185:Artificial Intelligence; D016000:Cluster Analysis; D020000:Decision Support Systems, Clinical; D003663:Decision Trees; D005260:Female; D005269:Femur / Q000473:pathology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D016011:Normal Distribution; D058729:Peripheral Arterial Disease / Q000175:diagnosis* / Q000628:therapy*; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity