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:
- w1=abs(M1(class 1,x)−M1(class 2,x))/(Std1(class1)+Std1(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)=Σl=1,vabs(Vlx−Vld)·wl;
- (III) designing a vector of required variable changes, defined as:
- deltaFx,d=(deltaVlx,d), for l=1,v as follows:
- deltaVlx, d=Vlx−Vld, 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.