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Başlık: A clinical decision support system for femoral peripheral arterial disease treatment
Yazarlar: Uludağ Üniversitesi/Mühendislik Fakültesi/Endüstri Mühendisliği Bölümü.
0000-0002-9220-7353
0000-0003-2978-2811
Yurtkuran, Alkın
Tok, Mustafa
Emel, Erdal
N-8691-2014
AAH-1410-2021
26031880400
6506976035
6602919521
Anahtar kelimeler: Mathematical & computational biology
Chonic obstructive pulmonary
Acute myocardial-infarction
Function neural networks
Multilayer perceptron
Diabetes disease
Heart-failure
Diagnosis
Classification
Algorithms
Stenosis
Cardiovascular surgery
Decision support systems
Diagnosis
Functions
Heat conduction
Image segmentation
K-means clustering
Multilayer neural networks
Pareto principle
Radial basis function networks
Assessment tool
Clinical decision support systems
Healthcare services
Patient record
Performance indicators
Peripheral arterial disease
Radial basis function neural networks
Radial basis functions
Diseases
Yayın Tarihi: 2013
Yayıncı: Hindawi
Atıf: Yurtkuran, A. vd. (2013). "A clinical decision support system for femoral peripheral arterial disease treatment", Computational and Mathematical Methods in Medicine, 2013.
Özet: 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.
URI: https://doi.org/10.1155/2013/898041
https://www.hindawi.com/journals/cmmm/2013/898041/
http://hdl.handle.net/11452/33150
ISSN: 1748-670X
1748-6718
Koleksiyonlarda Görünür:Scopus
Web of Science

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