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Internet Electronic Journal of Molecular Design - IEJMD, ISSN 1538-6414, CODEN IEJMAT
ABSTRACT - Internet Electron. J. Mol. Des. August 2002, Volume 1, Number 8, 418-427

Support Vector Machines for Cancer Diagnosis from the Blood Concentration of Zn, Ba, Mg, Ca, Cu, and Se
Ovidiu Ivanciuc
Internet Electron. J. Mol. Des. 2002, 1, 418-427

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Abstract:
Machine learning techniques, mainly artificial neural networks, clustering and classification algorithms, have recently received considerable attention as successful methods for modeling medical data. Using a wide variety of mathematical equations, machine learning algorithms are able to generate predictive models for different cancer types. Support vector machines (SVM) are a new machine learning algorithm that found numerous applications in bioinformatics, cheminformatics, computational biology, and structure-activity relationships. In this study we have investigated the application of SVM for cancer diagnosis from the blood concentration of Zn, Ba, Mg, Ca, Cu, and Se. The SVM model with the best prediction power was identified by a leave-10%-out cross-validation procedure, using the dot, polynomial, radial basis function, neural, and anova kernels. Extensive simulations demonstrate that the classification performances of SVM depend strongly on the kernel type and various parameters that control the kernel shape. The best prediction results were obtained with a dot kernel with seven support vectors. The anova kernel offered comparable predictions, but with 24 support vectors. Support vector machines represent a powerful and flexible classification algorithm, with many potential applications in modeling medical data. The results reported in the present study demonstrate such an application in the cancer diagnosis.

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