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.

Human voice reacts very sensitively to human's minute physical condition For instance, human voice disorders affect patients, profoundly especially in the case of Parkinson's disease Acoustic tools such as MDVP, can function as an equipment that measures

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 regarding statistical distribution. The MTS performs Taguchi's fractional factorial design based on the Mahahlanobis distance as a performance metric. In this work, MTS used for analyzing Wisconsin Breast Cancer data which have 10 attributes. With the data, which have 10 different tests, collected from patients determine who has cancer or not. At this research MTS used for reducing the number of test with define the relationship between each attrivute and diagnosis result. The accuracy of diagnosis compare to previous research.

  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

Mahalanobis Taguchi System (MTS) is developed by Genishi Taguchi as a part of his quality engineering methodology. The basic idea of Taguchi's quality engineering is looking for the way of effectiveness of analyzing multivariate system. In the MTS, with the standardized variables of healthy normal data, Mahalanobis Distance(MD) calculated and that can be discriminate between normal and abnormal objects. If this discrimination process is successful, next step is optimization which is try to reduce number of attributes by neglecting less effective attributes to MD. Orthogonal Array (OA) and Signal to Noise ratio (S/N) are used to evaluate the amount contribution of each attribute to the MD. Wisconsin Breast Cancer study, from machining learning repository at University of California at Irvine, used for examining the discriminant ability of MTS.