Data analysis and predictive systems and related methodologies

Data analysis and predictive systems and related methodologies

  1. A computer controlled process of optimising a model Mx suitable for use in data analysis and determining a prognostic outcome specific to a particular subject (input vector x), the subject comprising a number of variable features in relation to a scenario of interest for which there is a global dataset D of samples also having the same features relating to the scenario, and for which the outcome is known, the method comprising the steps of: a) determining what number and a subset Vx of variable features will be used in assessing the outcome for the input vector x; b) determining what number Kx of samples from within the global data set D will form a neighbourhood Dx about x; c) selecting suitable Kx samples from the global data set which have the variable features that most closely accord to the variable features of the particular subject x to form the neighbourhood Dx; d) ranking the Vx variable features within the neighbourhood Dx in order of importance to the outcome of vector x and obtaining a weight vector Wx for all variable features Vx; e) creating a prognostic model Mx, having a set of model parameters Px and the other parameters from steps a) -d); f) testing the accuracy of the model Mx at step e) for each sample from Dx; g) storing both the accuracy from step f), and the model parameters developed in steps a) to e); h) repeating steps a) and/or b) whilst applying an optimisation procedure to optimise Vx and/or Kx,to determine their optimal values, before repeating steps c)-h) until maximum accuracy at step f) is achieved.
Priority Date

15/10/2008

PCT and National Phases
Country Application or Grant Number Status
United States US 20110307228 A1 Pending
Europe EP 2350966 A1 Pending
New Zealand 572036 Granted