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Comparison of Machine-Learning Classification Models for Glaucoma Management

This study develops an objective machine-learning classification model for classifying glaucomatous optic discs and reveals the classificatory criteria to assist in clinical glaucoma management. In this study, 163 glaucoma eyes were labelled with four optic disc types by three glaucoma specialists a...

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Autores principales: An, Guangzhou, Omodaka, Kazuko, Tsuda, Satoru, Shiga, Yukihiro, Takada, Naoko, Kikawa, Tsutomu, Nakazawa, Toru, Yokota, Hideo, Akiba, Masahiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6029465/
https://www.ncbi.nlm.nih.gov/pubmed/30018755
http://dx.doi.org/10.1155/2018/6874765
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author An, Guangzhou
Omodaka, Kazuko
Tsuda, Satoru
Shiga, Yukihiro
Takada, Naoko
Kikawa, Tsutomu
Nakazawa, Toru
Yokota, Hideo
Akiba, Masahiro
author_facet An, Guangzhou
Omodaka, Kazuko
Tsuda, Satoru
Shiga, Yukihiro
Takada, Naoko
Kikawa, Tsutomu
Nakazawa, Toru
Yokota, Hideo
Akiba, Masahiro
author_sort An, Guangzhou
collection PubMed
description This study develops an objective machine-learning classification model for classifying glaucomatous optic discs and reveals the classificatory criteria to assist in clinical glaucoma management. In this study, 163 glaucoma eyes were labelled with four optic disc types by three glaucoma specialists and then randomly separated into training and test data. All the images of these eyes were captured using optical coherence tomography and laser speckle flowgraphy to quantify the ocular structure and blood-flow-related parameters. A total of 91 parameters were extracted from each eye along with the patients' background information. Machine-learning classifiers, including the neural network (NN), naïve Bayes (NB), support vector machine (SVM), and gradient boosted decision trees (GBDT), were trained to build the classification models, and a hybrid feature selection method that combines minimum redundancy maximum relevance and genetic-algorithm-based feature selection was applied to find the most valid and relevant features for NN, NB, and SVM. A comparison of the performance of the three machine-learning classification models showed that the NN had the best classification performance with a validated accuracy of 87.8% using only nine ocular parameters. These selected quantified parameters enabled the trained NN to classify glaucomatous optic discs with relatively high performance without requiring color fundus images.
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spelling pubmed-60294652018-07-17 Comparison of Machine-Learning Classification Models for Glaucoma Management An, Guangzhou Omodaka, Kazuko Tsuda, Satoru Shiga, Yukihiro Takada, Naoko Kikawa, Tsutomu Nakazawa, Toru Yokota, Hideo Akiba, Masahiro J Healthc Eng Research Article This study develops an objective machine-learning classification model for classifying glaucomatous optic discs and reveals the classificatory criteria to assist in clinical glaucoma management. In this study, 163 glaucoma eyes were labelled with four optic disc types by three glaucoma specialists and then randomly separated into training and test data. All the images of these eyes were captured using optical coherence tomography and laser speckle flowgraphy to quantify the ocular structure and blood-flow-related parameters. A total of 91 parameters were extracted from each eye along with the patients' background information. Machine-learning classifiers, including the neural network (NN), naïve Bayes (NB), support vector machine (SVM), and gradient boosted decision trees (GBDT), were trained to build the classification models, and a hybrid feature selection method that combines minimum redundancy maximum relevance and genetic-algorithm-based feature selection was applied to find the most valid and relevant features for NN, NB, and SVM. A comparison of the performance of the three machine-learning classification models showed that the NN had the best classification performance with a validated accuracy of 87.8% using only nine ocular parameters. These selected quantified parameters enabled the trained NN to classify glaucomatous optic discs with relatively high performance without requiring color fundus images. Hindawi 2018-06-19 /pmc/articles/PMC6029465/ /pubmed/30018755 http://dx.doi.org/10.1155/2018/6874765 Text en Copyright © 2018 Guangzhou An et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
An, Guangzhou
Omodaka, Kazuko
Tsuda, Satoru
Shiga, Yukihiro
Takada, Naoko
Kikawa, Tsutomu
Nakazawa, Toru
Yokota, Hideo
Akiba, Masahiro
Comparison of Machine-Learning Classification Models for Glaucoma Management
title Comparison of Machine-Learning Classification Models for Glaucoma Management
title_full Comparison of Machine-Learning Classification Models for Glaucoma Management
title_fullStr Comparison of Machine-Learning Classification Models for Glaucoma Management
title_full_unstemmed Comparison of Machine-Learning Classification Models for Glaucoma Management
title_short Comparison of Machine-Learning Classification Models for Glaucoma Management
title_sort comparison of machine-learning classification models for glaucoma management
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6029465/
https://www.ncbi.nlm.nih.gov/pubmed/30018755
http://dx.doi.org/10.1155/2018/6874765
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