Open Access Research

An approach to predict the risk of glaucoma development by integrating different attribute data

Yuichi Tokuda1, Tomohito Yagi1, Kengo Yoshii1, Yoko Ikeda2, Masahiro Fuwa3, Morio Ueno2, Masakazu Nakano1, Natsue Omi1, Masami Tanaka1, Kazuhiko Mori2, Masaaki Kageyama3, Ikumitsu Nagasaki4, Katsumi Yagi5, Shigeru Kinoshita2 and Kei Tashiro1*

Author Affiliations

1 Department of Genomic Medical Sciences, Kyoto Prefectural University of Medicine, Kajiicho 465, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto, 602-8566, Japan

2 Department of Ophthalmology, Kyoto Prefectural University of Medicine, Kajiicho 465, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto, 602-8566, Japan

3 Research and Development Center, Santen Pharmaceutical Co. Ltd, 8916-16 Takayama-cho, Ikoma, Nara, 630-0101, Japan

4 Department of Mathematics, Kyoto Prefectural University of Medicine, Kajiicho 465, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto, 602-8566, Japan

5 Louis Pasteur Center for Medical Research, 103-5, Tanakamonzen-cho, Sakyo-ku, Kyoto City, Kyoto, 606-8225, Japan

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SpringerPlus 2012, 1:41  doi:10.1186/2193-1801-1-41

Published: 24 October 2012

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.

Keywords:
Glaucoma; GWAS; Machine learning; Integration approach