Comparison of Ocular Biomechanical Machine Learning Classifiers for Glaucoma Diagnosis

Shu Hao Lu; Ka Yue Lee; Jones Iok Tong Chong; Andrew K.C. Lam; Jimmy S.M. Lai; David C.C. Lam

doi:10.1109/BIBM.2018.8621238

Comparison of Ocular Biomechanical Machine Learning Classifiers for Glaucoma Diagnosis

Shu Hao Lu, Ka Yue Lee, Jones Iok Tong Chong, Andrew K.C. Lam, Jimmy S.M. Lai, David C.C. Lam

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

1 Citation (Scopus)

Abstract

Application of machine learning methodology on patient functional and structural data has been shown to improve glaucoma classification accuracy. Intraocular pressure (IOP) and the biomechanical behaviors of the eye are early indicators of glaucoma. Four classifiers: linear logistic regression, support vector machine, random forest classifier and gradient boosting classifier are tested for discrimination using a 52-patient biomechanical data set that includes 20 glaucoma (40 eyes) and 32 healthy subjects (64 eyes). Results show that the 98.3% accuracy from linear logistic regression (LLR) is the highest correlation accuracy amongst the tested methods. The LLR classification accuracy is comparable with classification accuracies attained for classification using functional and structural measurements from image data sets. Since IOP elevation and biomechanical changes often precede imageable symptoms, the new biomechanics diagnostic classifier maybe used as a detection method for early stage glaucoma.

Original language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Editors	Harald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2539-2543
Number of pages	5
ISBN (Electronic)	9781538654880
DOIs	https://doi.org/10.1109/BIBM.2018.8621238
Publication status	Published - 24 Jan 2019
Event	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain Duration: 3 Dec 2018 → 6 Dec 2018

Publication series

Name	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Country/Territory	Spain
City	Madrid
Period	3/12/18 → 6/12/18

Keywords

glaucoma diagnosis
machine learning
ocular biomechanics

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics

More information

10.1109/BIBM.2018.8621238

Cite this

Lu, S. H., Lee, K. Y., Chong, J. I. T., Lam, A. K. C., Lai, J. S. M., & Lam, D. C. C. (2019). Comparison of Ocular Biomechanical Machine Learning Classifiers for Glaucoma Diagnosis. In H. Schmidt, D. Griol, H. Wang, J. Baumbach, H. Zheng, Z. Callejas, X. Hu, J. Dickerson, & L. Zhang (Eds.), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 (pp. 2539-2543). Article 8621238 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2018.8621238

Lu, Shu Hao ; Lee, Ka Yue ; Chong, Jones Iok Tong et al. / Comparison of Ocular Biomechanical Machine Learning Classifiers for Glaucoma Diagnosis. Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. editor / Harald Schmidt ; David Griol ; Haiying Wang ; Jan Baumbach ; Huiru Zheng ; Zoraida Callejas ; Xiaohua Hu ; Julie Dickerson ; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 2539-2543 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

@inproceedings{a0a8829689a940b5b22633988db84789,

title = "Comparison of Ocular Biomechanical Machine Learning Classifiers for Glaucoma Diagnosis",

abstract = "Application of machine learning methodology on patient functional and structural data has been shown to improve glaucoma classification accuracy. Intraocular pressure (IOP) and the biomechanical behaviors of the eye are early indicators of glaucoma. Four classifiers: linear logistic regression, support vector machine, random forest classifier and gradient boosting classifier are tested for discrimination using a 52-patient biomechanical data set that includes 20 glaucoma (40 eyes) and 32 healthy subjects (64 eyes). Results show that the 98.3% accuracy from linear logistic regression (LLR) is the highest correlation accuracy amongst the tested methods. The LLR classification accuracy is comparable with classification accuracies attained for classification using functional and structural measurements from image data sets. Since IOP elevation and biomechanical changes often precede imageable symptoms, the new biomechanics diagnostic classifier maybe used as a detection method for early stage glaucoma.",

keywords = "glaucoma diagnosis, machine learning, ocular biomechanics",

author = "Lu, {Shu Hao} and Lee, {Ka Yue} and Chong, {Jones Iok Tong} and Lam, {Andrew K.C.} and Lai, {Jimmy S.M.} and Lam, {David C.C.}",

year = "2019",

month = jan,

day = "24",

doi = "10.1109/BIBM.2018.8621238",

language = "English",

series = "Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2539--2543",

editor = "Harald Schmidt and David Griol and Haiying Wang and Jan Baumbach and Huiru Zheng and Zoraida Callejas and Xiaohua Hu and Julie Dickerson and Le Zhang",

booktitle = "Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018",

note = "2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 ; Conference date: 03-12-2018 Through 06-12-2018",

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Lu, SH, Lee, KY, Chong, JIT, Lam, AKC, Lai, JSM & Lam, DCC 2019, Comparison of Ocular Biomechanical Machine Learning Classifiers for Glaucoma Diagnosis. in H Schmidt, D Griol, H Wang, J Baumbach, H Zheng, Z Callejas, X Hu, J Dickerson & L Zhang (eds), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018., 8621238, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Institute of Electrical and Electronics Engineers Inc., pp. 2539-2543, 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Madrid, Spain, 3/12/18. https://doi.org/10.1109/BIBM.2018.8621238

Comparison of Ocular Biomechanical Machine Learning Classifiers for Glaucoma Diagnosis. / Lu, Shu Hao; Lee, Ka Yue; Chong, Jones Iok Tong et al.
Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. ed. / Harald Schmidt; David Griol; Haiying Wang; Jan Baumbach; Huiru Zheng; Zoraida Callejas; Xiaohua Hu; Julie Dickerson; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. p. 2539-2543 8621238 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Comparison of Ocular Biomechanical Machine Learning Classifiers for Glaucoma Diagnosis

AU - Lu, Shu Hao

AU - Lee, Ka Yue

AU - Chong, Jones Iok Tong

AU - Lam, Andrew K.C.

AU - Lai, Jimmy S.M.

AU - Lam, David C.C.

PY - 2019/1/24

Y1 - 2019/1/24

N2 - Application of machine learning methodology on patient functional and structural data has been shown to improve glaucoma classification accuracy. Intraocular pressure (IOP) and the biomechanical behaviors of the eye are early indicators of glaucoma. Four classifiers: linear logistic regression, support vector machine, random forest classifier and gradient boosting classifier are tested for discrimination using a 52-patient biomechanical data set that includes 20 glaucoma (40 eyes) and 32 healthy subjects (64 eyes). Results show that the 98.3% accuracy from linear logistic regression (LLR) is the highest correlation accuracy amongst the tested methods. The LLR classification accuracy is comparable with classification accuracies attained for classification using functional and structural measurements from image data sets. Since IOP elevation and biomechanical changes often precede imageable symptoms, the new biomechanics diagnostic classifier maybe used as a detection method for early stage glaucoma.

AB - Application of machine learning methodology on patient functional and structural data has been shown to improve glaucoma classification accuracy. Intraocular pressure (IOP) and the biomechanical behaviors of the eye are early indicators of glaucoma. Four classifiers: linear logistic regression, support vector machine, random forest classifier and gradient boosting classifier are tested for discrimination using a 52-patient biomechanical data set that includes 20 glaucoma (40 eyes) and 32 healthy subjects (64 eyes). Results show that the 98.3% accuracy from linear logistic regression (LLR) is the highest correlation accuracy amongst the tested methods. The LLR classification accuracy is comparable with classification accuracies attained for classification using functional and structural measurements from image data sets. Since IOP elevation and biomechanical changes often precede imageable symptoms, the new biomechanics diagnostic classifier maybe used as a detection method for early stage glaucoma.

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M3 - Conference article published in proceeding or book

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T3 - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

SP - 2539

EP - 2543

BT - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

A2 - Schmidt, Harald

A2 - Griol, David

A2 - Wang, Haiying

A2 - Baumbach, Jan

A2 - Zheng, Huiru

A2 - Callejas, Zoraida

A2 - Hu, Xiaohua

A2 - Dickerson, Julie

A2 - Zhang, Le

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Y2 - 3 December 2018 through 6 December 2018

ER -

Lu SH, Lee KY, Chong JIT, Lam AKC, Lai JSM, Lam DCC. Comparison of Ocular Biomechanical Machine Learning Classifiers for Glaucoma Diagnosis. In Schmidt H, Griol D, Wang H, Baumbach J, Zheng H, Callejas Z, Hu X, Dickerson J, Zhang L, editors, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 2539-2543. 8621238. (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). doi: 10.1109/BIBM.2018.8621238

Comparison of Ocular Biomechanical Machine Learning Classifiers for Glaucoma Diagnosis

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