Development of glaucoma predictive model and risk factors assessment based on supervised models

Development of glaucoma predictive model and risk factors assessment based on supervised models

Objectives: To develop and to propose a machine learning model for predicting glaucoma and identifying its risk factors. Method: Data analysis pipeline is designed for this study based on Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. The main steps of the pipeline include d...

Full description

Bibliographic Details
Main Authors: Emamian, M.H (Author), Fotouhi, A. (Author), Hashemi, H. (Author), Khatibi, T. (Author), Sadat, S. (Author), Sharifi, M. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
adult
Article
bagging ensemble
classifier
controlled study
Data Mining
decision tree
diagnostic accuracy
Ensemble classification
extra tree
eye axis length
Feature selection
female
glaucoma
human
Imbalanced Learning
k nearest neighbor
machine learning
major clinical study
male
Ophthalmology
perimetry
predictive model
pupil barycenter on Y coordinate
random forest
receiver operating characteristic
risk factor
sensitivity and specificity
support vector machine
systolic blood pressure
vertical cup-to-disc ratio
visual field defect
visual system parameters
white to white diameter
Online Access:View Fulltext in Publisher

Internet

View Fulltext in Publisher

Similar Items