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) 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.