Colon cancer prediction with Genetic profiles using Intelligent techniques

Sm, Alladi; Ss, P; Ravi, R.; Us, Murthy

doi:10.6026/97320630003130

Cited by 36 publications

(13 citation statements)

References 7 publications

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“…SVM methods have been increasingly used in a wide variety of medical classification problems. In certain instances they can prove superior in terms of classification accuracy to standard methods such as logistic regression, especially in being able to extract key predictors [18][19][20][21] that can then be used in the simplified algorithm.…”

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%

“…The SVM has been shown to be extremely robust in solving prediction problems while handling large sets of predictors [18]. It is an alternative to more standard statistical techniques such as logistic regression and in certain situations has been found to be superior to logistic regression for finding a robust fit with fewer predictors [18][19][20][21]. …”

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A screening algorithm for HIV‐associated neurocognitive disorders

et al. 2010

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“…With feature selection using t-statistics, the authors selected 10 genes [37]. For the SVM-RBF method, the accuracy obtained was 85.4%.…”

Section: Related Workmentioning

confidence: 99%

Hybrid SPR algorithm to select predictive genes for effectual cancer classification

Sundaram¹,

Venkata²,

Parthasarathy³

2013

Turk J Elec Eng & Comp Sci

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Abstract:Designing an automated system for classifying DNA microarray data is an extremely challenging problem because of its high dimension and low amount of sample data. In this paper, a hybrid statistical pattern recognition algorithm is proposed to reduce the dimensionality and select the predictive genes for the classification of cancer. Colon cancer gene expression profiles having 62 samples of 2000 genes were used for the experiment. A gene subset of 6 highly informative genes was selected by the algorithm, which provided a classification accuracy of 93.5%.

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“…According to the authors, this technique improves the consistency of the SVM classifier, and also allows for the extraction of higher-level biological data which is available in other databases and formats. In [32] performance of SVM is investigated with linear regression and neural network on colon tumor data sets after performing feature selection. 10 and 50 features were selected by t-statistic feature selection method and achieved maximum of 85% accuracy on SVM with RBF kernel.…”

Section: Related Workmentioning

confidence: 99%