![]() Center for Machine Learning and Intelligent Systems |
About
Citation Policy
Donate a Data Set
Contact
View ALL Data Sets |
Source: Guilherme de Alencar Barreto (guilherme '@' deti.ufc.br) & Ajalmar Rêgo da Rocha Neto (ajalmar '@' ifce.edu.br), Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza, Ceará, Brazil.
Data Set Information: Biomedical data set built by Dr. Henrique da Mota during a medical residence period in the Group of Applied Research in Orthopaedics (GARO) of the Centre Médico-Chirurgical de Réadaptation des Massues, Lyon, France. The data have been organized in two different but related classification tasks. The first task consists in classifying patients as belonging to one out of three categories: Normal (100 patients), Disk Hernia (60 patients) or Spondylolisthesis (150 patients). For the second task, the categories Disk Hernia and Spondylolisthesis were merged into a single category labelled as 'abnormal'. Thus, the second task consists in classifying patients as belonging to one out of two categories: Normal (100 patients) or Abnormal (210 patients). We provide files also for use within the WEKA environment. 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):40–47.
Citation Request: Please refer to the Machine Learning Repository's citation policy |
Supported By: |
![]() |
In Collaboration With: |
![]() |