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Article
MentalSquares − A Generic Bipolar Support Vector Machine for Mental Disorder Classification, Diagnostic Analysis, and Neurobiological Data Mining
International Journal of Data Mining and Bioinformatics
  • Wen-Rang Zhang, Georgia Southern University
  • A. K. Pandurangi
  • Karl E. Peace, Georgia Southern University
  • Yan-Qing Zhang, Georgia State University
Document Type
Article
Publication Date
1-1-2010
Disciplines
Abstract

MentalSquares (MSQs)--an equilibrium-based dimensional approach is presented for the classification and diagnostic analysis of psychological conditions with Bipolar Disorders (BPDs) as an example. While a Support Vector Machine (SVM) is defined in Hilbert space. A MSQ can be considered as a generic SVM for improved classification. Different from the traditional categorical model of BPDs, the generic approach focuses on the balance of two poles of mental equilibrium. Preliminary results show that this new approach has a number of advantages over existing models. The generic model is analytically illustrated with public domain clinical examples and well-known empirical clinical knowledge. Its clinical and computerised operability is illustrated. Its potential of being a practical method for the classification and analysis of neurobiological patterns and drug effects is discussed.

Citation Information
Wen-Rang Zhang, A. K. Pandurangi, Karl E. Peace and Yan-Qing Zhang. "MentalSquares − A Generic Bipolar Support Vector Machine for Mental Disorder Classification, Diagnostic Analysis, and Neurobiological Data Mining" International Journal of Data Mining and Bioinformatics (2010)
Available at: http://works.bepress.com/wen-ran_zhang/6/