Evaluation of machine learning classifiers in keratoconus detection from orbscan II examinations
Clinics
;
65(12): 1223-1228, 2010. graf, tab
Article
in English
| LILACS
| ID: lil-578558
ABSTRACT
PURPOSE:
To evaluate the performance of support vector machine, multi-layer perceptron and radial basis function neural network as auxiliary tools to identify keratoconus from Orbscan II maps.METHODS:
A total of 318 maps were selected and classified into four categories normal (n = 172), astigmatism (n = 89), keratoconus (n = 46) and photorefractive keratectomy (n = 11). For each map, 11 attributes were obtained or calculated from data provided by the Orbscan II. Ten-fold cross-validation was used to train and test the classifiers. Besides accuracy, sensitivity and specificity, receiver operating characteristic (ROC) curves for each classifier were generated, and the areas under the curves were calculated.RESULTS:
The three selected classifiers provided a good performance, and there were no differences between their performances. The area under the ROC curve of the support vector machine, multi-layer perceptron and radial basis function neural network were significantly larger than those for all individual Orbscan II attributes evaluated (p<0.05).CONCLUSION:
Overall, the results suggest that using a support vector machine, multi-layer perceptron classifiers and radial basis function neural network, these classifiers, trained on Orbscan II data, could represent useful techniques for keratoconus detection.
Full text:
Available
Index:
LILACS (Americas)
Main subject:
Artificial Intelligence
/
Corneal Topography
/
Keratoconus
Type of study:
Diagnostic study
/
Evaluation studies
/
Prognostic study
Limits:
Adult
/
Female
/
Humans
/
Male
Language:
English
Journal:
Clinics
Journal subject:
Medicine
Year:
2010
Type:
Article
Affiliation country:
Brazil
Institution/Affiliation country:
Universidade de São Paulo/BR
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