MentalSquares: A generic bipolar Support Vector Machine for psychiatric disorder classification, diagnostic analysis and neurobiological data mining

  • Authors:

  • Wen-Ran Zhang;Anand K. Pandurangi;Karl E. Peace;Yan-Qing Zhang;Zhongming Zhao

  • Affiliations:

  • Computer Science Department, Georgia Southern University, Statesboro, GA 30460, USA.;Department of Psychiatry, Virginia Commonwealth University, Richmond, VA 23298, USA.;Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA 30460, USA.;Computer Science Department, Georgia State University, Atlanta, GA 30302, USA.;Vanderbilt University Medical Center, Departments of Biomedical Informatics, Psychiatry, and Cancer Biology, Nashville, Tennessee 37232, USA

  • Venue:
  • International Journal of Data Mining and Bioinformatics
  • Year:
  • 2011

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Abstract

MentalSquares (MSQs) â聙聯 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.