Table 1 Summary of the previous studies considering glaucoma prediction, screening and/or diagnosis
From: Development of glaucoma predictive model and risk factors assessment based on supervised models
Authors | Target | Type of Dataset | Dataset | Classifier | |
|---|---|---|---|---|---|
Diagnosis | Do Screening or Not | ||||
(Li et al., 2019)[31] | Yes | Yes | Structured | SAP Data | LDA, SVM, NB, ANN |
(Liu et al., 2013)[10] | Yes | Yes | Structured, Images and Genes | Personal Data, Fundus Images, Genome Data | SVM MKL |
(Li et al., 2018)[11] | Yes | Yes | Structured | Visual Field Repots | SVM, RF, K-NN, CNN |
(Noronha et al., 2019)[25] | Yes | No | Images | Fundus Image | SVM, NB |
(Yo and Hong, 2015)[28] | Yes | No | Structured | Clinical Variables | MLR, ANN |
(Li et al., 2020)[29] | Yes | Yes | Structured and Images | Fundus Image, Medical History Data | RNN(ResNet101) |
(Acharya et al., 2017)[26] | Yes | No | Images | Fundus Images | DT, QDA, LDA, SVM, KNN, PNN |
(Mookiah et al., 2012)[27] | Yes | Yes | Images | Fundus Images | SVM |
(Chai et al., 2018)[32] | Yes | No | Structured and Images | Fundus Images, Clinical Data | Multi-Branch Neural Network |
(Pathan et al., 2021)[33] | Yes | Yes | Images | Fundus Images | ANN, SVM, AdaBoost |
(Kim et al., 2017)[30] | Yes | No | Structured | RNFL Thickness, Visual Field test Parameter, General Ophthalmic Examination | RF, DT, SVM, KNN |