Algorithm 1. MO-SEHM.

1:  Inputs: Dataset D = ${\sum }_{i=1}^N$ (Xi, Yi).

2:  Outputs: Optimal solution for HF mortality.

3:  Initial-Phase: Develop the SEHM.

4:   $D{T}_m\leftarrow$ Generate the number decision tree

5:  for $i\leftarrow$ 1; $i\le m$; i ++ do

6:      $B_s\leftarrow$ bootstrap samples from Dtrain

7:      $D{T}_i\leftarrow$ built decision trees

8:      $B{P}_i\leftarrow$ producing the base predictions

9:  end for

10:  for $j\leftarrow$ 1; $j\le f$; k++ do

11:      $M{F}_j\leftarrow$ generating the meta-features using BP

12:      $SEHM\leftarrow$ evaluate MF using LG

13:  end for

14:  Final-Phase: Apply NSGA-II and integrate SEHM.

15:  Initialize parameters: population (P), generations (G),…,

16:   $L\leftarrow$ length of input features

17:  for $l\leftarrow$ 1; $l\le$ L; l++ do

18:     for $g\leftarrow$ 1; $g\le$ G; g++ do

19:        for $p\leftarrow$ 1; $p\le$ P; p++ do

20:            $Fitnes{s}_p\leftarrow SEHM\left(X_p^{(SEF)}\right)$

21:        end for

22:         $NextGEN\leftarrow NSGAII(P,Fitness)$

23:     end for

24:  end for

25:  $PF\leftarrow IdentifyPF(NextGEN,Fitness)$

26:  $OptimalSolution\leftarrow ChooseOptimalSolutation(PF)$