Moorhead, K.T.Lee, D.S.Chase, GeoffMoot, A.R.Ledingham, K.Scotter, J.Allardyce, R.Sentilomohan, S.T.Endre, Z.2009-05-132009-05-132008Moorhead, K.T., Lee, D.S., Chase, J.G., Moot, A.R., Ledingham, K., Scotter, J., Allardyce, R., Sentilomohan, S.T., Endre, Z. (2008) Classifying algorithms for SIFT-MS technology and medical diagnosis. Computer Methods and Programs in Biomedicine, 89(3), pp. 226-238.0169-2607http://hdl.handle.net/10092/2443Selected 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.enkernel classifierclassificationSIFT-MSdiagnosticsbreath analysisVOCJournal ArticleFields of Research::280000 Information, Computing and Communication Sciences::280400 Computation Theory and MathematicsFields of Research::250000 Chemical Sciences::250400 Analytical Chemistry::250402 Analytical spectrometryhttps://doi.org/10.1016/j.cmpb.2007.11.011