The Mahalanobis-Taguchi System is a diagnosis and predictive method for analyzing patterns in multivariate cases. The goal of this study is diagnosis of the spondylolisthesis from biomedical data that is derived from the shape and orientation of the pelvis and lumbar spine. The data set has six attributes including pelvic incidence, pelvic tilt, lumbar lordosis angle, sacral slope, pelvic radius and grade of spondylolisthesis and two class including normal and abnormal. From University of California at Irvine machine learning repository, 100 normal and 150 spondylolisthesis patient’s data were used for this study. Mahalanobis Taguchi System (MTS) application process and the diagnosis results were described in this paper.

The Mahalanobis-Taguchi System is a diagnosis and predictive method for analyzing patterns in multivariate cases. The goal of this study is diagnosis of the spondylolisthesis using biomedical data that is derived from the shape and orientation of the pelvis and lumbar spine. The data set has six attributes including pelvic incidence, pelvic tilt, lumbar lordosis angle, sacral slope, pelvic radius and grade of spondylolisthesis and two class including normal and abnormal. From UCI machine learning repository, 100 normal and 150 spondylolisthesis patient’s data used for this study. Mahalanobis Taguchi System(MTS) application process and the diagnosis results described in this paper.