Input: 1. Target dataset $T_{Atr}$ ; 2) Response variable CU; 3) The number of respondents N;

Output: 1. Model selected M_k; 2. The metrics $HMM\left(m,r\right)$ ; 3) Knowledge representation: CU

1. Start

2. for $m=1,2,\cdots ,k$ do

3. Compute ROC Values for each model

4. Assign weight for each model based on user’s requirements

5. Compute Confusion matrix for each model

6. Assign weight for each model based on user’s requirements

7. Test the effect of Data Imbalance problem for each model

8. Test Statistical Significance using F-test

9. Practicability and Applicability: Identify as yes or no

10. Simplicity of Model Interpretation: Identify as yes or no

11. Established knowledge: Identify as Positive, Negative, Neutral, Unstudied

12. Computational cost: identify as High, Moderate, and Low

13. Compute the hybrid multidimensional metrics using equation (4)

14. Find $m,r$ s.t $HM{M}_{mr}=\max \left(HMM\left(m,r\right)\right)$

15. end for

16. return M_k; _{$HMM\left(m,r\right)$} ; CU