Harun Karamanli1, Tankut Yalcinoz2, Mehmet Akif Yalcinoz3, Tuba Yalcinoz3. 1. Department of Pulmonology, Faculty of Medicine, Mevlana University, Aksinne Neighborhood Esmetas Street No: 16, 42040, Meram-Konya, Turkey. drharun@hotmail.com. 2. Department of Electrical and Electronics Engineering, Mevlana University, Konya, Turkey. 3. Barış Cad, Konya, 42003, Turkey.
Abstract
BACKGROUND: Recently, artificial neural networks (ANNs) have been widely applied in science, engineering, and medicine. In the present study, we evaluated the ability of artificial neural networks to be used as a computer program and assistant tool in the diagnosis of obstructive sleep apnea (OSA). Our hypothesis was that ANNs could use clinical information to precisely predict cases of OSA. METHOD: The study population in this clinical trial consisted of 201 patients with suspected OSA (140 with a positive diagnosis of OSA and 61 with a negative diagnosis of OSA). The artificial neural network was trained by assessing five clinical variables from 201 patients; efficiency was then estimated in this group of 201 patients. The patients were classified using a five-element input vector. ANN classifiers were assessed with the multilayer perceptron (MLP) networks. RESULTS: Use of the MLP classifiers resulted in a diagnostic accuracy of 86.6 %, which in clinical practice is high enough to reduce the number of patients evaluated by polysomnography (PSG), an expensive and limited diagnostic resource. CONCLUSIONS: By establishing a pattern that allows the recognition of OSA, ANNs can be used to identify patients requiring PSG.
BACKGROUND: Recently, artificial neural networks (ANNs) have been widely applied in science, engineering, and medicine. In the present study, we evaluated the ability of artificial neural networks to be used as a computer program and assistant tool in the diagnosis of obstructive sleep apnea (OSA). Our hypothesis was that ANNs could use clinical information to precisely predict cases of OSA. METHOD: The study population in this clinical trial consisted of 201 patients with suspected OSA (140 with a positive diagnosis of OSA and 61 with a negative diagnosis of OSA). The artificial neural network was trained by assessing five clinical variables from 201 patients; efficiency was then estimated in this group of 201 patients. The patients were classified using a five-element input vector. ANN classifiers were assessed with the multilayer perceptron (MLP) networks. RESULTS: Use of the MLP classifiers resulted in a diagnostic accuracy of 86.6 %, which in clinical practice is high enough to reduce the number of patients evaluated by polysomnography (PSG), an expensive and limited diagnostic resource. CONCLUSIONS: By establishing a pattern that allows the recognition of OSA, ANNs can be used to identify patients requiring PSG.
Authors: Ana M Andrés-Blanco; Daniel Álvarez; Andrea Crespo; C Ainhoa Arroyo; Ana Cerezo-Hernández; Gonzalo C Gutiérrez-Tobal; Roberto Hornero; Félix Del Campo Journal: PLoS One Date: 2017-11-27 Impact factor: 3.240
Authors: Daniela Ferreira-Santos; Pedro Amorim; Tiago Silva Martins; Matilde Monteiro-Soares; Pedro Pereira Rodrigues Journal: J Med Internet Res Date: 2022-09-30 Impact factor: 7.076