@article{oai:kanazawa-u.repo.nii.ac.jp:00008507,
 author = {渡辺, 哲陽 and 米山, 猛 and Watanabe, Tetsuyou and Yoneyama, Takeshi and Toribatake, Yasumitsu and Hayashi, Hiroyuki},
 issue = {6611021},
 journal = {Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS},
 month = {Jan},
 note = {There are multiple diseases that cause intermittent claudication, including lumber spinal canal stenosis (LSS) and peripheral arterial disease (PAD). LSS is categorized on the basis of the diseased part: L4 and L5. The medical treatment for these groups is totally different and the differentiation is important. With this in mind, we examined walking-motion data for patients and derived several features for the differentiation in previous studies. However, these features were not specialized for classification, and there is no guarantee that the features are effective for real differentiation. The present study investigates the possibility of differentiation by gait analysis, via use of an L1-regularized support vector machine (SVM). An L1-regularized SVM can execute both classification and feature selections simultaneously. On the basis of this method, our paper presents the methodology for classifying the underlying disease of the intermittent claudication with an accuracy of 79.7%. In addition, new effective features for the differentiation are extracted. © 2013 IEEE.},
 pages = {6409--6412},
 title = {Main disease classification of intermittent claudication via L1-regularized SVM},
 year = {2013}
}