Automated Decision Tree Classification of Corneal Shape

作者：Michael D. Twa, Srinivasan Parthasarathy, Cynthia J. Roberts, Ashraf M. Mahmoud, Thomas W. Raasch, Mark A. Bullimore · 发表于：Optometry and Vision Science · 年份：2005 · DOI：10.1097/01.opx.0000192350.01045.6f · 被引用次数：106 · 研究领域：Corneal surgery and disorders、Ophthalmology and Visual Impairment Studies、Ocular Surface and Contact Lens

PURPOSE: The volume and complexity of data produced during videokeratography examinations present a challenge of interpretation. As a consequence, results are often analyzed qualitatively by subjective pattern recognition or reduced to comparisons of summary indices. We describe the application of decision tree induction, an automated machine learning classification method, to discriminate between normal and keratoconic corneal shapes in an objective and quantitative way. We then compared this method with other known classification methods. METHODS: The corneal surface was modeled with a seventh-order Zernike polynomial for 132 normal eyes of 92 subjects and 112 eyes of 71 subjects diagnosed with keratoconus. A decision tree classifier was induced using the C4.5 algorithm, and its classification performance was compared with the modified Rabinowitz-McDonnell index, Schwiegerling's Z3 index (Z3), Keratoconus Prediction Index (KPI), KISA%, and Cone Location and Magnitude Index using recommended classification thresholds for each method. We also evaluated the area under the receiver operator characteristic (ROC) curve for each classification method. RESULTS: Our decision tree classifier performed equal to or better than the other classifiers tested: accuracy was 92% and the area under the ROC curve was 0.97. Our decision tree classifier reduced the information needed to distinguish between normal and keratoconus eyes using four of 36 Zernike polynomial coefficients. The four sur...