Development of machine learning models for diagnosis of glaucoma

PLoS ONE

Volumn 12, Issue 5, 2017, Pages

  Kim, Seong Jae ^a   Cho, Kyong Jin ^b   Oh, Sejong ^b  

^a Gyeongsang National University School of Medicine   (South Korea)

^b Dankook University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; AREA UNDER THE CURVE; ARTICLE; CLINICAL EVALUATION; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; FALSE NEGATIVE RESULT; FEMALE; GLAUCOMA; HUMAN; K NEAREST NEIGHBOR; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; MEDICAL DECISION MAKING; MIDDLE AGED; PREDICTION; RANDOM FOREST; RECEIVER OPERATING CHARACTERISTIC; RETINAL NERVE FIBER LAYER THICKNESS; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; VISUAL FIELD; CLASSIFICATION; COMPUTER ASSISTED DIAGNOSIS; DECISION TREE; DIAGNOSTIC IMAGING; INFORMATION PROCESSING; PERIMETRY; PROCEDURES; RETINA;

ADULT; AREA UNDER CURVE; DATASETS AS TOPIC; DECISION TREES; DIAGNOSIS, COMPUTER-ASSISTED; FEMALE; GLAUCOMA; HUMANS; MACHINE LEARNING; MALE; MIDDLE AGED; RETINA; ROC CURVE; VISUAL FIELD TESTS;

EID: 85019629418     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0177726     Document Type: Article

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