Sensitivity and specificity of machine learning classifiers and spectral domain OCT for the diagnosis of glaucoma
| Autor: | Fabrício R Silva, Fernanda Cremasco, Marcelo Dias, Vital Paulino Costa, V. G. Vidotti, Edson Satoshi Gomi, Graziela Massa Resende |
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| Rok vydání: | 2012 |
| Předmět: |
genetic structures
Glaucoma Machine learning computer.software_genre 03 medical and health sciences 0302 clinical medicine Optical coherence tomography medicine AdaBoost medicine.diagnostic_test Receiver operating characteristic business.industry General Medicine medicine.disease eye diseases Visual field Random forest Support vector machine Ophthalmology Multilayer perceptron 030221 ophthalmology & optometry sense organs Artificial intelligence business computer 030217 neurology & neurosurgery |
| Zdroj: | European journal of ophthalmology. |
| ISSN: | 1724-6016 |
| Popis: | Purpose. To investigate the sensitivity and specificity of machine learning classifiers (MLC) and spectral domain optical coherence tomography (SD-OCT) for the diagnosis of glaucoma. Methods. Sixty-two patients with early to moderate glaucomatous visual field damage and 48 healthy individuals were included. All subjects underwent a complete ophthalmologic examination, achromatic standard automated perimetry, and RNFL imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec, Inc., Dublin, California, USA). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters. Subsequently, the following MLCs were tested: Classification Tree (CTREE), Random Forest (RAN), Bagging (BAG), AdaBoost M1 (ADA), Ensemble Selection (ENS), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Naive-Bayes (NB), and Support Vector Machine (SVM). Areas under the ROC curves (aROCs) obtained for each parameter and each MLC were compared. Results. The mean age was 57.0±9.2 years for healthy individuals and 59.9±9.0 years for glaucoma patients (p=0.103). Mean deviation values were -4.1±2.4 dB for glaucoma patients and -1.5±1.6 dB for healthy individuals (p |
| Databáze: | OpenAIRE |
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