Title :
Main disease classification of intermittent claudication via L1-regularized SVM
Author :
Watanabe, Toshio ; Yoneyama, Takashi ; Toribatake, Y. ; Hayashi, H.
Author_Institution :
Sch. of Mech. Eng., Kanazawa Univ., Kakuma-machi, Japan
Abstract :
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.
Keywords :
diseases; feature extraction; gait analysis; medical image processing; support vector machines; L1-regularized SVM; L1-regularized support vector machine; LSS; PAD; differentiation; disease classification; feature extraction; feature selections; gait analysis; intermittent claudication; lumber spinal canal stenosis; peripheral arterial disease; walking-motion data; Accuracy; Cameras; Diseases; Feature extraction; Legged locomotion; Muscles; Support vector machines;
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE
Conference_Location :
Osaka
DOI :
10.1109/EMBC.2013.6611021