title: Cross-Examination for Angle-Closure Glaucoma Feature Detection.
abstract: Effective feature selection plays a vital role in anterior segment imaging for determining the mechanism involved in angle-closure glaucoma (ACG) diagnosis. This research focuses on the use of redundant features for complex disease diagnosis such as ACG using anterior segment optical coherence tomography images. Both supervised [minimum redundancy maximum relevance (MRMR)] and unsupervised [Laplacian score (L-score)] feature selection algorithms have been cross-examined with different ACG mechanisms. An AdaBoost machine learning classifier is then used for classifying the five various classes of ACG mechanism such as iris roll, lens, pupil block, plateau iris, and no mechanism using both feature selection methods. The overall accuracy has shown that the usefulness of redundant features by L-score method in improved ACG diagnosis compared to minimum redundant features by MRMR method.
authors.forename: Swamidoss Issac
authors.initials: SI
authors.lastname: Niwas
authors.forename: Weisi
authors.initials: W
authors.lastname: Lin
authors.forename: Chee Keong
authors.initials: CK
authors.lastname: Kwoh
authors.forename: C-C Jay
authors.initials: CC
authors.lastname: Kuo
authors.forename: Chelvin C
authors.initials: CC
authors.lastname: Sng
authors.forename: Maria Cecilia
authors.initials: MC
authors.lastname: Aquino
authors.forename: Paul T K
authors.initials: PT
authors.lastname: Chew
mesh_terms: D000465:Algorithms; D015812:Glaucoma, Angle-Closure / Q000175:diagnosis*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D011237:Predictive Value of Tests; D041623:Tomography, Optical Coherence / Q000379:methods*