Corrigendum to “Fast and efficient lung disease classification using hierarchical one-against-all support vector machine and cost-sensitive feature selection” [Comput. Biol. Med. 42 (2012) 1157–1164]

Lee, Youngjoo; Chang, Yongjun; Kim, Namkug; Lim, Jae Jun; Seo, Joon Beom; Lee, Young Kyung

doi:10.1016/j.compbiomed.2013.05.001

Cited by 1 publication

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“…Recently, SVM learning has been widely used for solving classification problems in the medical field. [10][11][12][13][14][15] This study was performed in 2 phases. In phase 1, gait characteristics of L4 and L5 radiculopathy in LSS patients were identified.…”

Section: Umbar Spinal Canal Stenosis (Lss)mentioning

confidence: 99%

Gait Analysis Using a Support Vector Machine for Lumbar Spinal Stenosis

Hayashi¹,

Toribatake²,

Murakami³

et al. 2015

Orthopedics

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Lumbar spinal canal stenosis (LSS) is diagnosed based on physical examination and radiological documentation of lumbar spinal canal narrowing. Differential diagnosis of the level of lumbar radiculopathy is difficult in multilevel spinal stenosis. Therefore, the authors focused on gait analysis as a classification method to improve diagnostic accuracy. The goal of this study was to identify gait characteristics of L4 and L5 radiculopathy in patients with LSS and to classify L4 and L5 radiculopathy using a support vector machine (SVM). The study group comprised 13 healthy volunteers (control group), 11 patients with L4 radiculopathy (L4 group), and 22 patients with L5 radiculopathy (L5 group). Light-emitting diode markers were attached at 5 sites on the affected side, and walking motion was analyzed using video recordings and the authors' development program. Potential gait characteristics of each group were identified to use as SVM parameters. In the knee joint of the L4 group, the waveform was similar to that of normal gait, but knee extension at initial contact was slightly greater than that of the other groups. In the ankle joint of the L5 group, the one-peak waveform pattern with disappearance of the second peak was present in 10 (45.5%) of 22 cases. The total classification accuracy was 80.4% using the SVM. The highest and lowest classification accuracies were obtained in the control group (84.6%) and the L4 group (72.7%), respectively. The authors' walking motion analysis system identified several useful factors for differentiating between healthy individuals and patients with L4 and L5 radiculopathy, with a high accuracy rate.

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