title: An approach to predict the risk of glaucoma development by integrating different attribute data.
abstract: Primary open-angle glaucoma (POAG) is one of the major causes of blindness worldwide and considered to be influenced by inherited and environmental factors. Recently, we demonstrated a genome-wide association study for the susceptibility to POAG by comparing patients and controls. In addition, the serum cytokine levels, which are affected by environmental and postnatal factors, could be also obtained in patients as well as in controls, simultaneously. Here, in order to predict the effective diagnosis of POAG, we developed an "integration approach" using different attribute data which were integrated simply with several machine learning methods and random sampling. Two data sets were prepared for this study. The one is the "training data set", which consisted of 42 POAG and 42 controls. The other is the "test data set" consisted of 73 POAG and 52 controls. We first examined for genotype and cytokine data using the training data set with general machine learning methods. After the integration approach was applied, we obtained the stable accuracy, using the support vector machine method with the radial basis function. Although our approach was based on well-known machine learning methods and a simple process, we demonstrated that the integration with two kinds of attributes, genotype and cytokines, was effective and helpful in diagnostic prediction of POAG.
authors: Department of Genomic Medical Sciences, Kyoto Prefectural University of Medicine, Kajiicho 465, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto, 602-8566 Japan.
authors: Yuichi
authors: Y
authors: Tokuda
authors: Tomohito
authors: T
authors: Yagi
authors: Kengo
authors: K
authors: Yoshii
authors: Yoko
authors: Y
authors: Ikeda
authors: Masahiro
authors: M
authors: Fuwa
authors: Morio
authors: M
authors: Ueno
authors: Masakazu
authors: M
authors: Nakano
authors: Natsue
authors: N
authors: Omi
authors: Masami
authors: M
authors: Tanaka
authors: Kazuhiko
authors: K
authors: Mori
authors: Masaaki
authors: M
authors: Kageyama
authors: Ikumitsu
authors: I
authors: Nagasaki
authors: Katsumi
authors: K
authors: Yagi
authors: Shigeru
authors: S
authors: Kinoshita
authors: Kei
authors: K
authors: Tashiro
keywords: GWAS; Glaucoma; Integration approach; Machine learning
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