Classifying patients based on six features
LicenseCC0: Public Domain
Tagshealth, health conditions
Context
The data have been organized in two different but related classification tasks.
- column3Cweka.csv (file with three class labels)
- 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).
- column2Cweka.csv (file with two class labels)
- 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).
Content
Field Descriptions:
Each patient is represented in the data set by six biomechanical attributes derived from the shape and orientation of the pelvis and lumbar spine (each one is a column):
- pelvic incidence
- pelvic tilt
- lumbar lordosis angle
- sacral slope
- pelvic radius
- grade of spondylolisthesis
Acknowledgements
The original dataset was downloaded from UCI ML repository:
Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science
Files were converted to CSV
Inspiration
Use these biomechanical features to classify patients according to their labels