Simultaneous classification and feature selection via convex quadratic programming with application to HIV-associated neurocognitive disorder assessment

Dunbar, Michelle; Murray, John M.; Cysique, Lucette A.; Brew, Bruce J.; Jeyakumar, V.

doi:10.1016/j.ejor.2010.03.017

Cited by 31 publications

(13 citation statements)

References 9 publications

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“…The SVM method used, pq À SVM, was a modification of the Lagrangian Support Vector Machine (LSVM) method of Mangasarian and Musicant [26], incorporating feature selection [27]. The inequality for prediction of NP impairment using the nonnormalized original data was scaled to a range of values of approximately À 10 to 1 10, for uniformity between scenarios.…”

Section: Discussionmentioning

confidence: 99%

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A screening algorithm for HIV‐associated neurocognitive disorders

et al. 2010

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BackgroundHIV physicians have limited time for cognitive screening. Here we developed an extra-brief, clinically based tool for predicting HIV-associated neurocognitive impairment (HAND) in order to determine which HIV-positive individuals require a more comprehensive neurological/ neuropsychological (NP) assessment. MethodsNinety-seven HIV-positive individuals with advanced disease recruited in an HIV out-patient clinic received standard NP testing. A screening algorithm was developed using support vector machines, an optimized prediction procedure for classifying individuals into two groups (here NP-impaired and NP-normal) based on a set of predictors. ResultsThe final algorithm utilized age, current CD4 cell count, past central nervous system HIV-related diseases and current treatment duration and required approximately 3 min to complete, with a good overall prediction accuracy of 78% (against the gold standard; NP-impairment status derived from standard NP testing) and a good specificity of 70%. ConclusionThis noncognitive-based algorithm should prove useful to identify HIV-infected patients with advanced disease at high risk of HAND who require more formal assessment. We propose staged guidelines, using the algorithm, for improved HAND therapeutic management. Future larger, international studies are planned to test the predictive effect of nadir CD4 cell count, hepatitis C virus infection, gender, ethnicity and HIV viral clade. We recommend the use of this first version for HIV-infected Caucasian men with advanced disease.Keywords: antiretroviral therapy, diagnosis algorithm, HIV/AIDS, HIV-associated neurocognitive disorder, mathematical model Accepted 21 January 2010 IntroductionThe clinical management of HIV-positive persons is increasingly complicated in the era of combination antiretroviral therapy (CART). One aspect of management that requires extensive training relates to the early identification of neurocognitive complications of HIV infection. In many countries the general practitioner or the HIV physician is often the primary patient's interlocutor [1]. Without specific screening using procedures that are still relatively lengthy or require specific training, especially for interpretation [2], physicians may sometimes overlook patients in need of further neurological evaluation.In the CART era, the prevalence of neurocognitive impairment remains high (up to 50% [3]) and HIVassociated neurocognitive disorder (HAND) has shifted towards a milder clinical presentation [4]. Such a mild clinical presentation can escape detection without formal neurological assessment and neuropsychological testing [5]. HAND, even in its mild form, is independently predictive of death [6] The development of the screening algorithm was based on support vector machine (SVM) methodology. Because the aim of our study was to provide a simplified algorithm from a complex set of predictors, SVM was the most appropriate procedure [18]. The SVM has been shown to be extremely robust in solving prediction problems while handling la...

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Section: Discussionmentioning

confidence: 99%

“…. ; 10g [27,28]. The steps of randomly choosing two-thirds of the data for training, the calculation of optimal parameters over the grid of values, and the choice of tuning parameters and predictor coefficients that achieve maximal testing efficiency were then repeated 1000 times.…”

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confidence: 99%

A screening algorithm for HIV‐associated neurocognitive disorders

et al. 2010

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“…Results for the Int method tend to be slightly better than those reported by Bradley and Mangasarian (2004) and comparable to the linear methods in Fung and Mangasarian (2004). Results for Int are much better than those reported by Dunbar et al [2010] for the wdbc data set, and slightly worse for the pima data set.…”

Section: Figure 1: Average Performance Relative To Bestmentioning

confidence: 54%

“…Guo and Dyer [2003] report good results when these techniques are applied in facial expression recognition. Dunbar et al [2010] formulate the simultaneous hyperplane placement and feature selection problem as a nonlinear support vector machine problem, and then reformulate it as a quadratic minimization problem subject to nonnegativity constraints. This is an extension of a method originally proposed by .…”

Section: Introductionmentioning

confidence: 99%

Integrated classifier hyperplane placement and feature selection

Chinneck

2012

Expert Systems with Applications

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Errata are shown in red. AbstractThe process of placing a separating hyperplane for data classification is normally disconnected from the process of selecting the features to use. An approach for feature selection that is conceptually simple but computationally explosive is to simply apply the hyperplane placement process to all possible subsets of features, selecting the smallest set of features that provides reasonable classification accuracy. Two ways to speed this process are (i) use a faster filtering criterion instead of a complete hyperplane placement, and (ii) use a greedy forward or backwards sequential selection method. This paper introduces a new filtering criterion that is very fast: maximizing the drop in the sum of infeasibilities in a linear-programming transformation of the problem. It also shows how the linear programming transformation can be applied to reduce the number of features after a separating hyperplane has already been placed while maintaining the separation that was originally induced by the hyperplane. Finally, a new and highly effective integrated method that simultaneously selects features while placing the separating hyperplane is introduced.

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“…The support vector machine has emerged as a powerful modelling tool for machine learning problems of data classification that arise in many areas of information and computer sciences [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%