Translational vision science & technology · 2020
Evaluating the Performance of Various Machine Learning Algorithms to Detect Subclinical Keratoconus.
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Cao Ke, Verspoor Karin, Sahebjada Srujana, Baird Paul N
Аннотация
PURPOSE: Keratoconus (KC) represents one of the leading causes of corneal transplantation worldwide. Detecting subclinical KC would lead to better management to avoid the need for corneal grafts, but the condition is clinically challenging to diagnose. We wished to compare eight commonly used machine learning algorithms using a range of parameter combinations by applying them to our KC dataset and build models to better differentiate subclinical KC from non-KC eyes.
METHODS: Oculus Pentacam was used to obtain corneal parameters on 49 subclinical KC and 39 control eyes, along with clinical and demographic parameters. Eight machine learning methods were applied to build models to differentiate subclinical KC from control eyes. Dominant algorithms were trained with all combinations of the considered parameters to select important parameter combinations. The performance of each model was evaluated and compared.
RESULTS: Using a total of eleven parameters, random forest, support vector machine and k-nearest neighbors had better performance in detecting subclinical KC. The highest area under the curve of 0.97 for detecting subclinical KC was achieved using five parameters by the random forest method. The highest sensitivity (0.94) and specificity (0.90) were obtained by the support vector machine and the k-nearest neighbor model, respectively.
CONCLUSIONS: This study showed machine learning algorithms can be applied to identify subclinical KC using a minimal parameter set that are routinely collected during clinical eye examination.
TRANSLATIONAL RELEVANCE: Machine learning algorithms can be built using routinely collected clinical parameters that will assist in the objective detection of subclinical KC.
METHODS: Oculus Pentacam was used to obtain corneal parameters on 49 subclinical KC and 39 control eyes, along with clinical and demographic parameters. Eight machine learning methods were applied to build models to differentiate subclinical KC from control eyes. Dominant algorithms were trained with all combinations of the considered parameters to select important parameter combinations. The performance of each model was evaluated and compared.
RESULTS: Using a total of eleven parameters, random forest, support vector machine and k-nearest neighbors had better performance in detecting subclinical KC. The highest area under the curve of 0.97 for detecting subclinical KC was achieved using five parameters by the random forest method. The highest sensitivity (0.94) and specificity (0.90) were obtained by the support vector machine and the k-nearest neighbor model, respectively.
CONCLUSIONS: This study showed machine learning algorithms can be applied to identify subclinical KC using a minimal parameter set that are routinely collected during clinical eye examination.
TRANSLATIONAL RELEVANCE: Machine learning algorithms can be built using routinely collected clinical parameters that will assist in the objective detection of subclinical KC.