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Journal of Healthcare Engineering
Volume 2018, Article ID 6874765, 8 pages
https://doi.org/10.1155/2018/6874765
Research Article

Comparison of Machine-Learning Classification Models for Glaucoma Management

1R&D Division, Topcon Corporation, Tokyo, Japan
2Cloud-Based Eye Disease Diagnosis Joint Research Team, RIKEN Center for Advanced Photonics, RIKEN, Wako, Japan
3Tohoku University Graduate School of Medicine, Sendai, Japan
4Image Processing Research Team, RIKEN Center for Advanced Photonics, RIKEN, Wako, Japan

Correspondence should be addressed to Masahiro Akiba; pj.oc.nocpot@abika-m

Received 27 December 2017; Revised 6 April 2018; Accepted 18 April 2018; Published 19 June 2018

Academic Editor: Altaf Hussain

Copyright © 2018 Guangzhou An et al. 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.

Abstract

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.