Vertebral Column

Donated on 8/8/2011

Data set containing values for six biomechanical features used to classify orthopaedic patients into 3 classes (normal, disk hernia or spondilolysthesis) or 2 classes (normal or abnormal).

Dataset Characteristics

Multivariate

Subject Area

Health and Medicine

Associated Tasks

Classification

Feature Type

Real

# Instances

310

# Features

6

Dataset Information

Additional 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.

Has Missing Values?

No

Variables Table

Variable NameRoleTypeDescriptionUnitsMissing Values
pelvic_incidenceFeatureContinuousno
pelvic_tiltFeatureContinuousno
lumbar_lordosis_angleFeatureContinuousno
sacral_slopeFeatureContinuousno
pelvic_radiusFeatureContinuousno
degree_spondylolisthesisFeatureContinuousno
classTargetCategoricalno

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Additional Variable 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).

Papers Citing this Dataset

Data Poisoning against Differentially-Private Learners: Attacks and Defenses

By Yuzhe Ma, Xiaojin Zhu, Justin Hsu. 2019

Published in ArXiv.

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Creators

Guilherme Barreto

Ajalmar Neto

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