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.

본 연구는 로컬 단위에서의 다변량 공간적 클러스터와 아웃라이어에 대한 분석에 대하여 논한다. 공간적 클러스터나 아웃라이어는 그 접근 방법이나 쓰임에 따라 다양한 정의를 내릴 수 있으나, 공간적 연관성을 기반으로 한다는 점에서는 근본적인 공통점이 있다. 그러나 현존하는 공간 연관성 척도들은 투입할 수 있는 변수의 수가 한정적이기 때문에 다변량 상황에서 공간적 연관성을 측정할 수 있는 방법에 대한 연구가 필요하다. 다변량 local 공간 연관성 척도의 개발을 위해, 본 연구에서는 두 집단간 분리 정도에 대한 측정이 가능한 마할라노비스 거리를 이용하였다. 마할라노비스 거리는 변수의 평균, 분산 그리고 변수간 공분산을 고려하여 계산이 된다. 본 연구에서 고안된 로컬 마할라노비스 거리는 해당 지역의 변수 벡터와 주변지역 변수의 평균 값 벡터를 통해 계산이 되며, 이를 수도권 지역 동읍면 단위에서 인구 전입/전출의 변수에 대해 적용하였다. 해당 단위에서의 공간적 변동은 카이제곱 p값 지도를 통해 확인할 수 있으며, 유의성 검정을 실시한 로컬 마할라노비스 거리 지도를 통해 인구 유출입 차원에서의 공간적 클러스터와 아웃라이어를 확인할 수 있다.

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.

High-performance concrete (HPC) is a new terminology used in concrete construction industry. Several studies have shown that concrete strength development is determined not only by the water-to-cement ratio but also influenced by the content of other conc

High-performance concrete(HPC) is a new terminology used in concrete construction industry. Several studies have shown that concrete strength development is determined not only by the water-to-cement ratio but also influenced by the content of other concrete ingredients. HPC is a highly complex material, which makes modeling its behavior a very difficult task. This paper aimed at demonstrating the possibilities of adapting artificial neural network (ANN) to predict the comprresive strength of HPC. Mahalanobis Distance(MD) outlier detection method used for the purpose increase prediction ability of ANN. The detailed procedure of calculating Mahalanobis Distance (MD) is described. The effects of outlier compared with before and after artificial neural network training. MD outlier detection method successfully removed existence of outlier and improved the neural network training and prediction perfomance.

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.