Explorit relationships among biomechanical features from data of orthopaedic patients
Published:
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. Each patient in the data set 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). In this exercise, we only focus on a binary classification task NO=0 and AB=1 In this Blog, I will walk through how do we predict a patient as Normal or Abnormal by training the model with 6 biomechanic features. The main method here is the K-nearest neighbor prediction. Two questions are addressed:
How many neighbors should we choose?
How large is my training set for good prediction?
Check Original Blog here.
