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Support vector machine case study

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Metrics details. We present a potentially useful alternative approach based on support vector machine SVM techniques to classify persons with and without common diseases. We illustrate the method to detect persons with diabetes and pre-diabetes in a cross-sectional representative sample of the U. The SVM models were used to select sets of variables that would yield the best classification of individuals into these diabetes categories. For Classification Scheme I, the set of diabetes-related variables with the best classification performance included family history, age, race and ethnicity, weight, height, waist circumference, body mass index BMI , and hypertension.
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Support Vector Machine Algorithm

Support vector machine case study
Support vector machine case study
Support vector machine case study
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Classifying data using Support Vector Machines(SVMs) in R - GeeksforGeeks

Include Synonyms Include Dead terms. Download full text. IR Applications. Volume In the ANN model, classification accuracy can be achieved by using the feed-forward of inputs, back-propagation of errors, and the adjustment of connection weights. In conjunction with the computational shortcut of kernel functions, the SVM classifier maps input data from the input space into the high-dimensional feature space, and seeks an optimal hyperplane to separate data from different classes. Both ANN and SVM machine learning algorithms can be used to establish nonlinear relationships between variables and rank the importance of variables, thereby, contributing to the effectiveness of medical curriculum assessment.
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International Journal of Computational Intelligence Systems

Background: Genetic, hormonal, and environmental factors contribute since infancy to sexual dimorphism in regional brain structures of subjects with typical development. However, the neuroanatomical differences between male and female children with autism spectrum disorders ASD are an intriguing and still poorly investigated issue. This study aims to evaluate whether the brain of young children with ASD exhibits sex-related structural differences and if a correlation exists between clinical ASD features and neuroanatomical underpinnings.
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Alexander Statnikov, Constantin F. Aliferis , Douglas P. Hardin, Isabelle Guyon.
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