Application of Machine Learning to Extract Decision Rules of Glaucoma Diagnosis
| Autor: | Jian-Cheng Lin, 林建成 |
|---|---|
| Rok vydání: | 2006 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 94 This study is to extract decision rules to differentiate between normal and glaucomatous eyes based on the quantitative assessment of summary data reports from the Stratus optical coherence tomography (OCT) in Taiwan Chinese population. Glaucoma is a kind of association disease. The disease can be affected by many factors, such as inherent genetic or external environment. The Stratus Optical Coherence Tomography (Stratus OCT) consisted of an infrared-sensitive video camera to provide a view of an low-coherence interferometer as light source and detection unit, a video monitor, a computer and an image analysis system. This research tries to combine self-organize map (SOM) and decision tree (DT) to discover hidden inherent rules and set up a distinguishing system of glaucoma. 121 glaucomatous patients and 121 normal patients were included in this study. Twelve decision rules were extracted and the accuracies for training sample and testing sample were 94.6% and 87.1%, respectively. Base on the result, we believe the combination of machine learning can enhance the diagnosis accuracy on medical disease. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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