Enhancement of Mahalanobis–Taguchi System via Rough Sets based Feature Selection

Iquebal, Ashif Sikandar; Pal, Avishek; Ceglarek, Darek; Tiwari, Manoj Kumar

doi:10.1016/j.eswa.2014.06.019

Cited by 33 publications

(14 citation statements)

References 30 publications

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“…A procedure for the health management of individuals comprises the following: (1) gather data about the health attributes of the individual, (2) calculate the gait abnormality value for the individual by determining the MD, and (3) offer the individual a high-effect prescription based on the health attributes to improve his/her health status [13,14,16,[30][31][32]44].…”

Section: Discussionmentioning

confidence: 99%

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Gait Analysis and Mathematical Index-Based Health Management Following Anterior Cruciate Ligament Reconstruction

et al. 2019

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Gait analysis is recognized as a method used in quantifying gait disorders and in clinical evaluations of patients. However, the current guidelines for the evaluation of post anterior cruciate ligament reconstruction (ACLR) patient outcomes are primarily based on qualitative assessments. This study aims to apply gait analyses and mathematical, index-based health management, using the Mahalanobis Taguchi System (MTS) and the Kanri Distance Calculator (KDC) to diagnose the level of the gait abnormality and to identify its contributing factors following ACLR. It is hypothesized that (1) the method is able to discriminate the gait patterns between a healthy group (HG) and patients with ACLR (PG), and (2) several contributing factors may affect ACLR patients’ rehabilitation performance. This study compared the gait of 10 subjects in the PG group with 15 subjects in the HG. The analysis was based on 11 spatiotemporal parameters. Gait data of all subjects were collected in a motion analysis laboratory. The data were then analyzed using MTS and KDC. In this study, two significant groups were recognized: the HG, who achieved results which were within the Mahalanobis space (MS), and (ii) the PG who achieved results above the MS. The results may be seen as being on-target and off-target, respectively. Based on the analysis, three variables (i.e., step width, single support time, and double support time) affected patient performance and resulted in an average mark of above 1.5 Mahalanobis distance (MD). The results indicated that by focusing on the contributing factors that affect the rehabilitation performance of the patients, it is possible to provide individualized and need-based treatment.

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Section: Discussionmentioning

confidence: 99%

“…In this approach, the level of abnormality is normally structured on a measure known as MD [12][13][14]16]. We needed to define the target group first.…”

Section: Diagnosismentioning

confidence: 99%

Gait Analysis and Mathematical Index-Based Health Management Following Anterior Cruciate Ligament Reconstruction

et al. 2019

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“…Improved MTS Algorithm. We used MTS for data classification [18][19][20][21][22][23][24][25]. In MTS, Mahalanobis space (MS; reference group) is obtained using standardized variables of healthy or normal data.…”

Section: Reducing Highly Correlatedmentioning

confidence: 99%

Diagnosis of Asthma Based on Routine Blood Biomarkers Using Machine Learning

Zhan

Chen

Cheng

et al. 2020

Computational Intelligence and Neuroscience

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Intelligent medical diagnosis has become common in the era of big data, although this technique has been applied to asthma only in limited contexts. Using routine blood biomarkers to identify asthma patients would make clinical diagnosis easier to implement and would enhance research of key asthma variables through data mining techniques. We used routine blood data from healthy individuals to construct a Mahalanobis space (MS). Then, we calculated Mahalanobis distances of the training routine blood data from 355 asthma patients and 1,480 healthy individuals to ensure the efficiency of MS. Orthogonal arrays and signal-to-noise ratios were used to optimize blood biomarker variables. Receiver operating characteristic (ROC) curve was used to determine the threshold value. Ultimately, we validated the system on 182 individuals based on the threshold value. Out of 35 patients with asthma, MTS correctly classified 94.15% of patients. In addition, 97.20% of 147 healthy individuals were correctly classified. The system isolated 7 routine blood biomarkers. Among these biomarkers, platelet distribution width, mean platelet volume, white blood cell count, eosinophil count, and lymphocyte ratio performed well in asthma diagnosis. In brief, MTS shows promise as an accurate method to identify asthma patients based on 7 vital blood biomarker variables and threshold determined by the ROC curve, thus offering the potential to simplify diagnostic complexity and optimize clinical efficiency.

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“…To handle the noisy and irrelevant features, one may apply feature selection (Fakhraei, Soltanian-Zadeh, & Fotouhi, 2014;Iquebal, Pal, Ceglarek, & Tiwari, 2014;Lin, Chen, & Wu, 2014;Li, Wu, Li, & Ding, 2013b to assign different weights to different features, so that the data samples could be represented in a better way than using the original features. So far, the most broadly used feature selection method is proposed by Sun et al (2012).…”

Section: Introductionmentioning

confidence: 99%

Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization

Wang

Huang

Sun

et al. 2015

Expert Systems with Applications

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a b s t r a c tNonnegative matrix factorization (NMF), a popular part-based representation technique, does not capture the intrinsic local geometric structure of the data space. Graph regularized NMF (GNMF) was recently proposed to avoid this limitation by regularizing NMF with a nearest neighbor graph constructed from the input data set. However, GNMF has two main bottlenecks. First, using the original feature space directly to construct the graph is not necessarily optimal because of the noisy and irrelevant features and nonlinear distributions of data samples. Second, one possible way to handle the nonlinear distribution of data samples is by kernel embedding. However, it is often difficult to choose the most suitable kernel. To solve these bottlenecks, we propose two novel graph-regularized NMF methods, AGNMF FS and AGNMF MK , by introducing feature selection and multiple-kernel learning to the graph regularized NMF, respectively. Instead of using a fixed graph as in GNMF, the two proposed methods learn the nearest neighbor graph that is adaptive to the selected features and learned multiple kernels, respectively. For each method, we propose a unified objective function to conduct feature selection/multi-kernel learning, NMF and adaptive graph regularization simultaneously. We further develop two iterative algorithms to solve the two optimization problems. Experimental results on two challenging pattern classification tasks demonstrate that the proposed methods significantly outperform state-of-the-art data representation methods.

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Enhancement of Mahalanobis–Taguchi System via Rough Sets based Feature Selection

Cited by 33 publications

References 30 publications

Gait Analysis and Mathematical Index-Based Health Management Following Anterior Cruciate Ligament Reconstruction

Gait Analysis and Mathematical Index-Based Health Management Following Anterior Cruciate Ligament Reconstruction

Diagnosis of Asthma Based on Routine Blood Biomarkers Using Machine Learning

Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization

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