脊柱生物医学数据集

Data Set Information: 由Henrique da Mota博士在法国里昂Massues适应医学中心矫形外科应用研究组（GARO）医疗居住期间建立的生物医学数据集。数据组织在两个不同但相关的分类任务中。第一项任务是将患者分为三类：正常（100名患者）、椎间盘突出（60名患者）或脊椎滑脱（150名患者）。对于第二项任务，椎间盘突出症和腰椎滑脱症被合并为一个单独的类别，标记为“异常”。因此，第二项任务包括将患者分为两类：正常（100名患者）或异常（210名患者）。我们还提供在WEKA环境中使用的文件。 Attribute Information: Each patient is represented in the data set by six biomechanical attributes derived from the shape and orientation of the pelvis and lumbar spine (in this order): pelvic incidence, pelvic tilt, lumbar lordosis angle, sacral slope, pelvic radius and grade of spondylolisthesis. The following convention is used for the class labels: DH (Disk Hernia), Spondylolisthesis (SL), Normal (NO) and Abnormal (AB). Relevant Papers: (1) Berthonnaud, E., Dimnet, J., Roussouly, P. & Labelle, H. (2005). 'Analysis of the sagittal balance of the spine and pelvis using shape and orientation parameters', Journal of Spinal Disorders & Techniques, 18(1):40a€“47. (2) Rocha Neto, A. R. & Barreto, G. A. (2009). 'On the Application of Ensembles of Classifiers to the Diagnosis of Pathologies of the Vertebral Column: A Comparative Analysis', IEEE Latin America Transactions, 7(4):487-496. (3) Rocha Neto, A. R., Sousa, R., Barreto, G. A. & Cardoso, J. S. (2011). 'Diagnostic of Pathology on the Vertebral Column with Embedded Reject Optiona€?, Proceedings of the 5th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA'2011), Gran Canaria, Spain, Lecture Notes on Computer Science, vol. 6669, p. 588-595. Citation Request: Please refer to the Machine Learning Repository's citation policy