@article{oai:junshin.repo.nii.ac.jp:00000233,
 author = {王丸, 愛子 and 小林, 龍徳 and KOBAYASHI, Tatsunori and 金, 珍澤 and Kim, Jintaek and Ohmaru, Aiko},
 issue = {7},
 journal = {純真学園大学雑誌, Journal of Junshin Gakuen University, Faculty of Health Sciences},
 month = {Mar},
 note = {Application/pdf, 要旨:　近年，ビッグデータ解析や人工知能の研究において判別分析や機械学習などの統計学的手法が利用されている．本研究では，判別分析と機械学習を簡便に実行することを目的とし，GNU Rを用いた統計処理プログラムを開発した．本プログラムは，GNU Rに含まれる識別器（線形判別分析と2次判別分析，Random Forest）を簡単なコマンド入力で迅速に実行できる．本プログラムの動作確認のため，オンライン公開されている乳がんデータベースの30の画像特徴量に基づいて，がんの良悪性の分類を行った．その結果，各識別器の正判別率は90%以上であり，分類結果は数秒で得られた．結論として，本プログラムは膨大な統計処理が求められる様々な領域において有用であると考えられ，一例として病理診断の補助的なツールとして応用できる可能性を示せた．
Abstract: In recent years, statistical methods such as discriminant analysis and machine learning are used in the research of Big Data analysis and AI. In this study, we developed a statistical processing program using GNU R, which aims to perform discriminant analysis and machine learning efficiently The program  enables us to run classifiers（linear discriminant analysis, quadratic discriminant analysis, and Random Forest）contained in GNU R uickly with a sim le command input. To confirm the operation of the program, we conducted a classification test of benign/malignant tumors based on the 30 feature values of images in the breast cancer database which is publicly available online. The test result was obtained within a few seconds, and the result showed that the percentage of correct discrimination at each classifier was more than 90％ In conclusion, the program was considered to be useful in various areas which require a significant amount of statistical processing, and as an example, we were able to demonstrate that this program can be applied as an additional useful tool to pathological diagnosis.},
 pages = {79--85},
 year = {2018},
 yomi = {オウマル, アイコ and コバヤシ, タツノリ and キム, ジンテ}
}