1. A computer implemented method of optimizing a transductive model Mx suitable for use in data analysis and determining a prognostic outcome specific to a particular subject x represented as input vector x, wherein input vector x comprises 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 variable features relating to the scenario as input vector x, and for which an outcome is known, the method comprising: (A) optimizing the transductive model by: a) determining what number and a subset Vx of variable features of input vector x will be used in assessing an outcome for the input vector x; b) determining what number Kx of samples from within the global data set D will form a neighborhood about input vector 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 neighborhood Dx; d) ranking the Vx variable features within the neighborhood Dx in order of importance to the outcome and obtaining a weight vector Wx for all variable features Vx; e) creating a prognostic transductive model Mx for each input vector x, having a set of model parameters Px and the other parameters Vx and Kx from elements a)-d); f) testing an accuracy of the model Mx for each sample from Dx method selected from the group consisting of: (i) calculating Wx as normalized SNR (Signal-to-Noise Ratio) coefficients and sorting the variables in descending order: V1, V2,. .. , Vv, where: w1>=w2>=. .. >=wy, calculated as follows: 
 w 1 =abs ( M 1 (class 1,x) −M 1 (class 2,x) )/( Std 1 (class1) +Std 1 (class2) ); (ii) testing for all variables Vx all possible combinations of values of their weights Wx are tested through an exhaustive search to maximize the overall accuracy of a model built on the data Dx; (iii) applying a genetic statistical analysis procedure, if the number of variables prevents using method (ii) above; (iv) applying a quantum inspired evolutionary statistical analysis technique, to select the optimal variable set Vx for every new input vector x and to weigh the variables through a probability wave function; g) storing both the accuracy and the set of model parameters; h) repeating elements a) and/or b) while applying an optimization procedure to optimize Vx and/or Kx, to determine their optimal values, before repeating elements c)-h) until the accuracy is maximized, wherein a number and a subset Vx of variable features of input vector x, and a number Kx of samples from within the global data set D that form a neighborhood about input vector x are determined anew each time elements a) and b) are repeated while applying an optimization procedure to optimize Vx and/or Kx; (B) determining a prognostic outcome y specific to the patient x using the optimized transductive model Mx by: (I) forming a vector: Fx={Vx,Wx,Kx,Dx,Mx,Px,t}, where the variable t represents the time of the model Mx creation; (II) calculating the weighted distance D(Fx,Fd) as an aggregated indication of how much a person's profile should change to reach an average desired profile Fd by using the following: 
 D ( Fx,Fd )=Σ l=1,v abs ( V lx −V ld )· w l ; (III) designing a vector of required variable changes, defined as: deltaFx,d=(deltaV lx,d ), for l=1,v as follows: deltaV lx, d =V lx −V ld , with an importance of: Wl; (C) modifying variable features Vx in the patient x to be closer to Kx values associated with an improved outcome relative to the prognostic outcome y determined for the patient x so as to improve the prognostic outcome of the patient x; and (D) repeating elements a) through h) to determine an improved prognostic outcome using re-optimized transductive model Mx.