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    Classifying algorithms for SIFT-MS technology and medical diagnosis

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    Author
    Moorhead, K.T.
    Lee, D.S.
    Chase, J.G.
    Moot, A.R.
    Ledingham, K.
    Scotter, J.
    Allardyce, R.
    Sentilomohan, S.T.
    Endre, Z.
    Date
    2008
    Permanent Link
    http://hdl.handle.net/10092/2443

    Selected Ion Flow Tube-Mass spectrometry (SIFT-MS) is an analytical technique for realtime quantification of trace gases in air or breath samples. SIFT-MS system thus offers unique potential for early, rapid detection of disease states. Identification of volatile organic compound (VOC) masses that contribute strongly towards a successful classification clearly highlights potential new biomarkers. A method utilising kernel density estimates is thus presented for classifying unknown samples. It is validated in a simple known case and a clinical setting before–after dialysis. The simple case with nitrogen in Tedlar bags returned a 100% success rate, as expected. The clinical proof-of-concept with seven tests on one patient had an ROC curve area of 0.89. These results validate the method presented and illustrate the emerging clinical potential of this technology.

    Subjects
    kernel classifier
     
    classification
     
    SIFT-MS
     
    diagnostics
     
    breath analysis
     
    VOC
     
    Fields of Research::280000 Information, Computing and Communication Sciences::280400 Computation Theory and Mathematics
     
    Fields of Research::250000 Chemical Sciences::250400 Analytical Chemistry::250402 Analytical spectrometry
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    • Engineering: Journal Articles [932]
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