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.

Mahalanobis Taguchi-System (MTS) has been used in different diagnostic applications to make quantitative decisions by constructing a multivariate system using data analytic methods without any assumption regarding statistical distribution. MTS performs Taguchi's fractional factorial design based on the Mahahlanobis distance as a performance metric. In this study, MTS used for analyzing automotive ride satisfaction, which measured as a CSR(Customer Satisfaction Rating). The automobile which has a good CSR score treated as a normal group for constructing Mahalanobis space. The results of this research show that two attribute (Impact Hardness and Memory Shake) have a minus gain value and can be removed from further analysis. With the linear regression model, the difference of CSR between using all 6 attributes and just using significant 4 attributes compared.

Mahalanobis Taguchi System (MTS) is a pattern information technology, which has been used in different diagnostic applications make quantitative decisions by constructing a multivariate measurement scale using data analytic methods without any assumption

  Mahalanobis Taguchi-System (MTS) is a pattern information technology, which has been used in different diagnostic applications to make quantitative decisions by constructing a multivariate system using data analytic methods without any assumption regard