NDLI: SVM and statistical technique method applying in Primary Open Angle Glaucoma diagnosis

Content Provider	IEEE Xplore Digital Library
Author	Lijun Cheng Yongsheng Ding
Copyright Year	2010
Description	Author affiliation: College of Information Sciences and Technology, Donghua University, Shanghai 201620, China (Lijun Cheng; Yongsheng Ding)
Abstract	The Primary Open Angle Glaucoma(POAG) discriminated model using support vector machine(SVC) method is presented to distinguish the primary open-angle glaucoma disease, which is not clear in early symptoms and involves in various risk factors, moreover easy to blind with prolonged intraocular hypertension. Through case study of clinical diagnosis, SVM classifier with a radial basis inner function was established to predict and discriminate some unknown patients in the paper. At the same time, Bayes angle discriminated model and Logistic regression, which are traditional statistical classification approaches, are set up to compare with SVM methods in POAG diagnosis. In the end, we conclude that SVM method is reliable and superior in many respects to statistical classification methods in the POAG recognition.
Starting Page	2973
Ending Page	2978
File Size	724126
Page Count	6
File Format	PDF
ISBN	9781424467129
e-ISBN	9781424467129
DOI	10.1109/WCICA.2010.5554175
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mathematical model Logistics Accuracy Support vector machine classification Data models Diseases Bayes Decision Theory SVM Open-angle Glaucoma Logistic Regression
Content Type	Text
Resource Type	Article

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