@article{oai:tohoku-mpu.repo.nii.ac.jp:00000099,
 author = {青木, 空眞 and Aoki, Sorama and 佐藤, 憲一 and Sato, Kenichi and 星, 憲司 and Hoshi, Kenji and 川上, 準子 and Kawakami, Junko and 森, 弘毅 and Mori, Kouki and 齋藤, 芳彦 and Saito, Yoshihiko and 吉田, 克己 and Yoshida, Katsumi},
 issue = {56},
 journal = {東北薬科大学研究誌, Journal of Tohoku Pharmaceutical University},
 month = {Dec},
 note = {In our previous papers we proposed a novel screening methodbthat assists the diagnosis of Grave's hyperthyrodism via two types of neural networks by making use of routine test data.This method can be applied by non-specialists during physical check-ups at a low cost and is expected to lead to rapid referrals for examination and treatment by thyroid specialists, that is,toimprove patient'QOL. In this report,we apply the support vector machine,which is a novel learning method building on kernels, to the classification problems of madical data such as Wisconsin breast cancer data or our screening of hyperthyroid.It turned out that the support vector machine ,after best turning of parameters based on the grid-search method,works quite well to correctly the lacated in the bordering area between two classes.Our results suggest that the SVM would work as a useful methods in our screening in addition to previous two types of neural networks.},
 pages = {67--74},
 title = {医療データ解析へのサポートベクトルマシン(SVM)の応用},
 year = {2009},
 yomi = {アオキ, ソラマ and サトウ, ケンイチ and ホシ, ケンジ and カワカミ, ジュンコ and モリ, コウキ and サイトウ, ヨシヒコ and ヨシダ, カツミ}
}