Using Inductive Logic Programming to Learn Rules that Identify Glaucomatous Eyes

This chapter examines the applicability and performance of Inductive Logic Programming (ILP) in learning classification rules for a medical domain. The domain is glaucoma diagnosis where ocular fundus images are used to identify glaucomatous eyes. An ILP system called GKS was developed, not only to deal with low-level measurement data such as images but also produce diagnostic rules that are readable and comprehensive for interactions with medical experts. Since such rules are directly used as diagnostic rules, the present method provides automatic construction of a knowledge base from an expert’s accumulated diagnostic experience. A variety of experiments are conducted to clarify the performance of classification based on the induced rules. The resulting performance is comparable with human-level classification. This indicates that an ILP-based method can be used as a highly-valuable medical decision tool.