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

Wen Ran Zhang, Anand K. Pandurangi, Karl E. Peace, Yan Qing Zhang, Zhongming Zhao

Research output: Contribution to journalArticlepeer-review

48 Scopus citations

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 thetraditional 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.

Original languageEnglish
Pages (from-to)532-557
Number of pages26
JournalInternational Journal of Data Mining and Bioinformatics
Volume5
Issue number5
DOIs
StatePublished - Oct 2011

Keywords

  • Computational neuroscience and psychiatry
  • Dimensional approach
  • Exploratory neurobiological data mining
  • Unified bipolar disorder classification
  • YinYang bipolar support vector machines

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